This document provides details on a proposed thesis project to develop software that uses clustering algorithms and data visualization to analyze high-dimensional clickstream data. The software would group website pages into clusters based on similarities in user behavior and display the results in a graphical user interface. This approach aims to provide insight into naturally occurring patterns in the data that could help website managers better understand user segments and behavior.
3 iaetsd semantic web page recommender systemIaetsd Iaetsd
This document discusses a semantic web-page recommender system that aims to improve upon traditional recommender systems. It proposes two novel knowledge representation models: 1) a semantic network that represents domain knowledge through terms, webpages, and relationships automatically constructed from website data, and 2) a conceptual prediction model that integrates semantic knowledge with web usage data to create a weighted semantic network for making recommendations. The system seeks to overcome limitations of prior systems by automating knowledge base construction and addressing the "new-item problem" through incorporation of semantic information. Evaluation shows the proposed approach yields better performance than existing web usage-based recommender systems.
AN INTEGRATED RANKING ALGORITHM FOR EFFICIENT INFORMATION COMPUTING IN SOCIAL...ijwscjournal
Social networks have ensured the expanding disproportion between the face of WWW stored traditionally in search engine repositories and the actual ever changing face of Web. Exponential growth of web users and the ease with which they can upload contents on web highlights the need of content controls on material published on the web. As definition of search is changing, socially-enhanced interactive search methodologies are the need of the hour. Ranking is pivotal for efficient web search as the search performance mainly depends upon the ranking results. In this paper new integrated ranking model based on fused rank of web object based on popularity factor earned over only valid interlinks from multiple social forums is proposed. This model identifies relationships between web objects in separate social networks based on the object inheritance graph. Experimental study indicates the effectiveness of proposed Fusion based ranking algorithm in terms of better search results.
Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...inventionjournals
The problem of web search time complexity and accuracy has been visited in many research papers, and the authors discussed many approaches to improve the search performance. Still the approaches does not produce any noticeable improvement and struggles with more time complexity as well. To overcome the issues identified, an efficient multi mode conceptual clustering algorithm has been discussed in this paper, which identifies the similar interested user groups by clustering their search context according to different conceptual queries. Identified user groups are shared with the related conceptual queries and their results to reduce the time complexity. The multi mode conceptual clustering, performs grouping of search queries and users according to number of users and their search pattern. The concept of search is identified by using Natural language processing methods and the web logs produced by the default web search engines. The author designed a dedicated web interface to collect the web log about the user search and the same data has been used to cluster the social groups according to number of conceptual queries. The search results has been shared between the users of identified social groups which reduces the search time complexity and improves the efficiency of web search in better manner
This document discusses distinguishing human users from bot users in web search logs. It proposes using multiple thresholds for different classification criteria rather than single thresholds, to avoid misclassifying ambiguous cases. It also defines "strong criteria" that identify activity levels unlikely or impossible for humans, to avoid false positives. The authors apply this approach to the AOL search log to classify over 92% of users as human and 0.6% as bots, with the rest unclassified. Humans tend to display consistent behavior while bots can vary widely between criteria.
This document provides an overview of nature-inspired methods that have been used in the Semantic Web for tasks like information retrieval, extraction, clustering, and personalization. It discusses how genetic algorithms, neural networks, fuzzy logic, and rough sets have helped with problems in these areas by modeling complex relationships and uncertainty. The document also describes approaches for representing uncertainty in ontologies, including using Bayesian networks to quantify overlap between concepts.
Zemanta is a content recommendation engine that provides relevant images, links, and related articles to enrich content for bloggers and authors. It analyzes text at the semantic level using its multiple data sources as well as DBpedia. Zemanta can be used through extensions and plugins or via its public API. While currently only supporting English, its use of universal terms allows multilingual recommendations by combining it with translation services and DBpedia's multilingual features.
The World Wide Web is booming and radically vibrant due to the well established standards and widely accountable framework which guarantees the interoperability at various levels of the application and the society as a whole. So far, the web has been functioning at the random rate on the basis of the human intervention and some manual processing but the next generation web which the researchers called semantic web, edging for automatic processing and machine-level understanding. The well set notion, Semantic Web would be turn possible if only there exists the further levels of interoperability prevails among the applications and networks. In achieving this interoperability and greater functionality among the applications, the W3C standardization has already released the well defined standards such as RDF/RDF Schema and OWL. Using XML as a tool for semantic interoperability has not achieved anything effective and failed to bring the interconnection at the larger level. This leads to the further inclusion of inference layer at the top of the web architecture and its paves the way for proposing the common design for encoding the ontology representation languages in the data models such as RDF/RDFS. In this research article, we have given the clear implication of semantic web research roots and its ontological background process which may help to augment the sheer understanding of named entities in the web.
Authorization mechanism for multiparty data sharing in social networkeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
3 iaetsd semantic web page recommender systemIaetsd Iaetsd
This document discusses a semantic web-page recommender system that aims to improve upon traditional recommender systems. It proposes two novel knowledge representation models: 1) a semantic network that represents domain knowledge through terms, webpages, and relationships automatically constructed from website data, and 2) a conceptual prediction model that integrates semantic knowledge with web usage data to create a weighted semantic network for making recommendations. The system seeks to overcome limitations of prior systems by automating knowledge base construction and addressing the "new-item problem" through incorporation of semantic information. Evaluation shows the proposed approach yields better performance than existing web usage-based recommender systems.
AN INTEGRATED RANKING ALGORITHM FOR EFFICIENT INFORMATION COMPUTING IN SOCIAL...ijwscjournal
Social networks have ensured the expanding disproportion between the face of WWW stored traditionally in search engine repositories and the actual ever changing face of Web. Exponential growth of web users and the ease with which they can upload contents on web highlights the need of content controls on material published on the web. As definition of search is changing, socially-enhanced interactive search methodologies are the need of the hour. Ranking is pivotal for efficient web search as the search performance mainly depends upon the ranking results. In this paper new integrated ranking model based on fused rank of web object based on popularity factor earned over only valid interlinks from multiple social forums is proposed. This model identifies relationships between web objects in separate social networks based on the object inheritance graph. Experimental study indicates the effectiveness of proposed Fusion based ranking algorithm in terms of better search results.
Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...inventionjournals
The problem of web search time complexity and accuracy has been visited in many research papers, and the authors discussed many approaches to improve the search performance. Still the approaches does not produce any noticeable improvement and struggles with more time complexity as well. To overcome the issues identified, an efficient multi mode conceptual clustering algorithm has been discussed in this paper, which identifies the similar interested user groups by clustering their search context according to different conceptual queries. Identified user groups are shared with the related conceptual queries and their results to reduce the time complexity. The multi mode conceptual clustering, performs grouping of search queries and users according to number of users and their search pattern. The concept of search is identified by using Natural language processing methods and the web logs produced by the default web search engines. The author designed a dedicated web interface to collect the web log about the user search and the same data has been used to cluster the social groups according to number of conceptual queries. The search results has been shared between the users of identified social groups which reduces the search time complexity and improves the efficiency of web search in better manner
This document discusses distinguishing human users from bot users in web search logs. It proposes using multiple thresholds for different classification criteria rather than single thresholds, to avoid misclassifying ambiguous cases. It also defines "strong criteria" that identify activity levels unlikely or impossible for humans, to avoid false positives. The authors apply this approach to the AOL search log to classify over 92% of users as human and 0.6% as bots, with the rest unclassified. Humans tend to display consistent behavior while bots can vary widely between criteria.
This document provides an overview of nature-inspired methods that have been used in the Semantic Web for tasks like information retrieval, extraction, clustering, and personalization. It discusses how genetic algorithms, neural networks, fuzzy logic, and rough sets have helped with problems in these areas by modeling complex relationships and uncertainty. The document also describes approaches for representing uncertainty in ontologies, including using Bayesian networks to quantify overlap between concepts.
Zemanta is a content recommendation engine that provides relevant images, links, and related articles to enrich content for bloggers and authors. It analyzes text at the semantic level using its multiple data sources as well as DBpedia. Zemanta can be used through extensions and plugins or via its public API. While currently only supporting English, its use of universal terms allows multilingual recommendations by combining it with translation services and DBpedia's multilingual features.
The World Wide Web is booming and radically vibrant due to the well established standards and widely accountable framework which guarantees the interoperability at various levels of the application and the society as a whole. So far, the web has been functioning at the random rate on the basis of the human intervention and some manual processing but the next generation web which the researchers called semantic web, edging for automatic processing and machine-level understanding. The well set notion, Semantic Web would be turn possible if only there exists the further levels of interoperability prevails among the applications and networks. In achieving this interoperability and greater functionality among the applications, the W3C standardization has already released the well defined standards such as RDF/RDF Schema and OWL. Using XML as a tool for semantic interoperability has not achieved anything effective and failed to bring the interconnection at the larger level. This leads to the further inclusion of inference layer at the top of the web architecture and its paves the way for proposing the common design for encoding the ontology representation languages in the data models such as RDF/RDFS. In this research article, we have given the clear implication of semantic web research roots and its ontological background process which may help to augment the sheer understanding of named entities in the web.
Authorization mechanism for multiparty data sharing in social networkeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Semantically enriched web usage mining for predicting user future movementsIJwest
Explosive and quick growth of the World Wide Web has resulted in intricate Web sites, demanding
enhanced user skills and sophisticated tools to help the Web user to find the desi
red information. Finding
desired information on the Web has become a critical ingredient of everyday personal, educational, and
business life. Thus, there is a demand for more sophisticated tools to help the user to navigate a Web site
and find the desired
information. The users must be provided with information and services specific to
their needs, rather than an undiffere
ntiated mass of information.
For discovering interesting and frequent
navigation patterns from Web server logs many Web usage mining te
chniques have been applied. The
recommendation accuracy of solely usage based techniques can be improved by integrating Web site
content and site structure in the personalization process.
Herein, we propose Semantically enriched Web Usage Mining method (S
WUM), which combines the fields
of Web Usage Mining and Semantic Web. In the proposed method, the undirected graph derived from
usage data is enriched with rich semantic information extracted from the Web pages and the Web site
structure. The experimental
results show that the SWUM generates accurate recommendations with
integration of usage, semantic data and Web site structure. The results shows that proposed method is able
to achieve 10
-
20%
better accuracy than the solely usage based model, and 5
-
8% bet
ter than an ontology
based model.
This document discusses methods for compressing mouse cursor activity data collected by websites for analytics purposes. It evaluates 10 compression algorithms, including 5 lossless and 5 lossy methods. The results show that different algorithms are suited to different goals, such as reducing bandwidth, improving client-side performance, or accurately replicating the original cursor data. Lossy algorithms like piecewise linear interpolation and distance-thresholding offered better performance and bandwidth reduction than lossless LZW compression. The study contributes to making mouse cursor tracking a practical technology by reducing the data size.
Implementation of Privacy Policy Specification System for User Uploaded Image...rahulmonikasharma
This document presents a proposed privacy policy specification system to help users control access to images shared over social media sites. The system would allow users to create groups, apply privacy policies (e.g. commenting, sharing, expiration, downloading) to each group, and then share images with specific groups so that the assigned group policies are applied to each image. This is intended to address limitations of existing systems that may inaccurately generate privacy policies if image metadata is unavailable or manually created. The proposed system aims to give users more control over privacy policies for shared images within designated groups on social media sites.
Entity linking with a knowledge baseissues, techniques, and solutionsShakas Technologies
The large number of potential applications from bridging Web data with knowledge bases has led to an increase in the entity linking research. Entity linking is the task to link entity mentions in text with their corresponding entities in a knowledgebase.
Web aggregation and mashup with kapow mashup serverYudep Apoi
The document provides an introduction and overview of the thesis which aims to develop a prototype portal for aggregating and comparing online booking information from multiple websites in Malaysia.
The introduction describes how the vast amount of structured data on the web provides motivation for novel methods to exploit this data. It also outlines the problem of comparing online booking information across different websites and the need for an automated system.
The document then discusses related works on travel search engines and portals that aggregate booking information from multiple sources. It also summarizes some literature on general web aggregation and mashups technologies as well as common techniques used like screen scraping.
Predicting Social Interactions from Different Sources of Location-based Knowl...Michael Steurer
Recent research has shown that digital online geo- location traces are new and valuable sources to predict social interactions between users, e.g. , check-ins via FourSquare or geo-location information in Flickr images. Interestingly, if we look at related work in this area, research studying the extent to which social interactions can be predicted between users by taking more than one location-based knowledge source into account does not exist. To contribute to this field of research, we have collected social interaction data of users in an online social network called My Second Life and three related location-based knowledge sources of these users (monitored locations, shared locations and favored locations), to show the extent to which social interactions between users can be predicted. Using supervised and unsupervised machine learning techniques, we find that on the one hand the same location-based features (e.g. the common regions and common observations) perform well across the three different sources. On the other hand, we find that the shared location information is better suited to predict social interactions between users than monitored or favored location information of the user.
Sampling of User Behavior Using Online Social NetworkEditor IJCATR
The popularity of online networks provides an opportunity to study the characteristics of online social network graphs is important, both to improve current systems and to design new application of online social networks. Although personalized search has been proposed for many years and many personalization strategies have been investigated, it is still unclear whether personalization is consistently effective on different queries for different users, and under different search contexts. In this paper, we study performance of information collection in a dynamic social network. By analyzing the results, we reveal that personalized search has significant improvement over common web search.
The mixing time of thee sampling process strongly depends on the characteristics of the graph.
IRJET-Model for semantic processing in information retrieval systemsIRJET Journal
This document proposes a model for semantic information retrieval that improves upon traditional keyword matching approaches. It involves three main components:
1. A crawling and indexing component that identifies websites and pages, extracts metadata, and generates a knowledge graph through semantic annotation.
2. A processing component that analyzes user queries and profiles to understand search intent, calculates semantic similarity between queries and indexed documents, and determines result relevance.
3. A presentation component that displays search results to users through both simple and advanced search interfaces, prioritizing the most relevant information based on the above processing.
The model is intended to address deficiencies in current Cuban web search by better understanding natural language queries and the contextual meaning of information through semantic technologies
Summary of Paper : Taxonomy of websearch by BroderBhavesh Singh
This document summarizes a paper that classified web search queries into three categories: navigational, informational, and transactional. Navigational queries aim to reach a specific website, informational queries seek information on a topic, and transactional queries want to perform an online activity like shopping. The paper found through surveys and query log analysis that around 20-25% of queries were navigational, 40-50% informational, and 25-35% transactional. It also proposed that early search engines only handled informational and navigational queries directly, while third generation engines aimed to better support all query types through semantic analysis and blending external databases.
This document discusses data mining techniques for social media. It explains that graph mining is used to cluster similar data together based on relationships and connections between nodes in a graph. Graph mining on Facebook can be used to search for friends, places, and interests while ranking results by strength of connections. Text mining extracts meaningful information from unstructured text data on social networks and can automatically process emails, classify texts, and potentially extract full information from websites.
Analysis, modelling and protection of online private data.Silvia Puglisi
Do we have online privacy? And what is privacy anyway in an online context?
This work aim at discovering what footprints users leave online and how these represent a threat to privacy.
This document discusses data mining in social networks. It covers topics like social network analysis, graph mining, and text mining on social media platforms. Graph mining is used to understand relationships and extract communities from social networks. Text mining techniques like clustering and anomaly detection are applied to textual data from blogs, messages, etc. on social platforms. The document also discusses accessing Facebook data through its API and SDK, and applications and limitations of social network analysis.
Travel Recommendation Approach using Collaboration Filter in Social NetworkingIRJET Journal
This document discusses a travel recommendation approach using collaboration filtering in social networks. It proposes personalized travel sequence recommendations based on travelogues and community photos posted on social media. Unlike other travel recommendation systems, it recommends a sequence of points of interest rather than individual locations. It maps user preferences and route descriptions to topic categories to calculate similarity and recommend routes. The system was evaluated on a dataset of over 7 million Flickr photos and 24,000 travelogues covering 864 travel locations in 9 cities.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Team of Rivals: UX, SEO, Content & Dev UXDC 2015Marianne Sweeny
The search engine landscape has changed dramatically and now relies heavily on user experience signals to influence rank in search results. In this presentation, I explore search engine methods for evaluating UX in a machine readable fashion and present a framework for successful cross-discipline collaboration.
The document discusses the concept of personalized web search (PWS). It notes that generic web search engines cannot identify different user needs, so PWS was introduced to personalize search results. Various techniques for PWS are discussed, including user profiling using demographic and interest data, hyperlink analysis, and community-based or location-based approaches. Maintaining accurate user profiles that respect privacy is also addressed. Potential applications and limitations of PWS are mentioned.
In this world of information technology, everyone has the tendency to do business electronically. Today
lot of businesses are happening on World Wide Web (WWW), it is very important for the website owner to
provide a better platform to attract more customers for their site. Providing information in a better way is
the solution to bring more customers or users. Customer is the end-user, who accessing the information
in a way it yields some credit to the web site owners. In this paper we define web mining and present a
method to utilize web mining in a better way to know the users and website behaviour which in turn
enhance the web site information to attract more users. This paper also presents an overview of the
various researches done on pattern extraction, web content mining and how it can be taken as a catalyst
for E-business.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
1) The document discusses the evolution of search engines and algorithms over time from early concepts like Hilltop and PageRank to more modern techniques like RankBrain that use neural networks.
2) It also examines how search engines have incorporated personalization and contextualization by using implicit and explicit user data and feedback to better understand search intent and tailor results.
3) Several studies summarized found that most users expect to find information within the first 2 minutes of searching, spend little time viewing individual results, and refine queries through an iterative process as understanding develops.
This document presents an algorithm for interactively learning monotone Boolean functions. The algorithm is based on Hansel's lemma, which states that algorithms based on finding maximal upper zeros and minimal lower units are optimal for learning monotone Boolean functions. The algorithm allows decreasing the number of queries needed to learn non-monotone functions that can be represented as combinations of monotone functions. The effectiveness of the approach is demonstrated through computational experiments in engineering and medical applications.
Word accessible - .:: NIB | National Industries for the Blind ::.butest
The document provides an agenda and information for the 2009 NIB/NAEPB Annual Training Conference held in Kansas City, Missouri from October 21-24, 2009. The conference focused on technology and strategic impacts with sessions addressing business topics to help agency leaders and employees. Activities included keynote speakers, committee meetings, tours of Alphapointe Association for the Blind, and an awards banquet honoring employee award winners. Presentations and materials from the conference were made available online.
The AgentMatcher system matches learners and learning objects (LOs) using a tree-structured representation of metadata. It extracts metadata from LOs using LOMGen and stores it in a database. Learners can enter query parameters as a weighted tree, which is compared to LO metadata trees to find similar LOs. Top matches above a similarity threshold are returned to the learner. LOMGen semi-automatically generates metadata using keywords and allows an administrator to refine selections. This enhances precision over simple keyword searches.
Semantically enriched web usage mining for predicting user future movementsIJwest
Explosive and quick growth of the World Wide Web has resulted in intricate Web sites, demanding
enhanced user skills and sophisticated tools to help the Web user to find the desi
red information. Finding
desired information on the Web has become a critical ingredient of everyday personal, educational, and
business life. Thus, there is a demand for more sophisticated tools to help the user to navigate a Web site
and find the desired
information. The users must be provided with information and services specific to
their needs, rather than an undiffere
ntiated mass of information.
For discovering interesting and frequent
navigation patterns from Web server logs many Web usage mining te
chniques have been applied. The
recommendation accuracy of solely usage based techniques can be improved by integrating Web site
content and site structure in the personalization process.
Herein, we propose Semantically enriched Web Usage Mining method (S
WUM), which combines the fields
of Web Usage Mining and Semantic Web. In the proposed method, the undirected graph derived from
usage data is enriched with rich semantic information extracted from the Web pages and the Web site
structure. The experimental
results show that the SWUM generates accurate recommendations with
integration of usage, semantic data and Web site structure. The results shows that proposed method is able
to achieve 10
-
20%
better accuracy than the solely usage based model, and 5
-
8% bet
ter than an ontology
based model.
This document discusses methods for compressing mouse cursor activity data collected by websites for analytics purposes. It evaluates 10 compression algorithms, including 5 lossless and 5 lossy methods. The results show that different algorithms are suited to different goals, such as reducing bandwidth, improving client-side performance, or accurately replicating the original cursor data. Lossy algorithms like piecewise linear interpolation and distance-thresholding offered better performance and bandwidth reduction than lossless LZW compression. The study contributes to making mouse cursor tracking a practical technology by reducing the data size.
Implementation of Privacy Policy Specification System for User Uploaded Image...rahulmonikasharma
This document presents a proposed privacy policy specification system to help users control access to images shared over social media sites. The system would allow users to create groups, apply privacy policies (e.g. commenting, sharing, expiration, downloading) to each group, and then share images with specific groups so that the assigned group policies are applied to each image. This is intended to address limitations of existing systems that may inaccurately generate privacy policies if image metadata is unavailable or manually created. The proposed system aims to give users more control over privacy policies for shared images within designated groups on social media sites.
Entity linking with a knowledge baseissues, techniques, and solutionsShakas Technologies
The large number of potential applications from bridging Web data with knowledge bases has led to an increase in the entity linking research. Entity linking is the task to link entity mentions in text with their corresponding entities in a knowledgebase.
Web aggregation and mashup with kapow mashup serverYudep Apoi
The document provides an introduction and overview of the thesis which aims to develop a prototype portal for aggregating and comparing online booking information from multiple websites in Malaysia.
The introduction describes how the vast amount of structured data on the web provides motivation for novel methods to exploit this data. It also outlines the problem of comparing online booking information across different websites and the need for an automated system.
The document then discusses related works on travel search engines and portals that aggregate booking information from multiple sources. It also summarizes some literature on general web aggregation and mashups technologies as well as common techniques used like screen scraping.
Predicting Social Interactions from Different Sources of Location-based Knowl...Michael Steurer
Recent research has shown that digital online geo- location traces are new and valuable sources to predict social interactions between users, e.g. , check-ins via FourSquare or geo-location information in Flickr images. Interestingly, if we look at related work in this area, research studying the extent to which social interactions can be predicted between users by taking more than one location-based knowledge source into account does not exist. To contribute to this field of research, we have collected social interaction data of users in an online social network called My Second Life and three related location-based knowledge sources of these users (monitored locations, shared locations and favored locations), to show the extent to which social interactions between users can be predicted. Using supervised and unsupervised machine learning techniques, we find that on the one hand the same location-based features (e.g. the common regions and common observations) perform well across the three different sources. On the other hand, we find that the shared location information is better suited to predict social interactions between users than monitored or favored location information of the user.
Sampling of User Behavior Using Online Social NetworkEditor IJCATR
The popularity of online networks provides an opportunity to study the characteristics of online social network graphs is important, both to improve current systems and to design new application of online social networks. Although personalized search has been proposed for many years and many personalization strategies have been investigated, it is still unclear whether personalization is consistently effective on different queries for different users, and under different search contexts. In this paper, we study performance of information collection in a dynamic social network. By analyzing the results, we reveal that personalized search has significant improvement over common web search.
The mixing time of thee sampling process strongly depends on the characteristics of the graph.
IRJET-Model for semantic processing in information retrieval systemsIRJET Journal
This document proposes a model for semantic information retrieval that improves upon traditional keyword matching approaches. It involves three main components:
1. A crawling and indexing component that identifies websites and pages, extracts metadata, and generates a knowledge graph through semantic annotation.
2. A processing component that analyzes user queries and profiles to understand search intent, calculates semantic similarity between queries and indexed documents, and determines result relevance.
3. A presentation component that displays search results to users through both simple and advanced search interfaces, prioritizing the most relevant information based on the above processing.
The model is intended to address deficiencies in current Cuban web search by better understanding natural language queries and the contextual meaning of information through semantic technologies
Summary of Paper : Taxonomy of websearch by BroderBhavesh Singh
This document summarizes a paper that classified web search queries into three categories: navigational, informational, and transactional. Navigational queries aim to reach a specific website, informational queries seek information on a topic, and transactional queries want to perform an online activity like shopping. The paper found through surveys and query log analysis that around 20-25% of queries were navigational, 40-50% informational, and 25-35% transactional. It also proposed that early search engines only handled informational and navigational queries directly, while third generation engines aimed to better support all query types through semantic analysis and blending external databases.
This document discusses data mining techniques for social media. It explains that graph mining is used to cluster similar data together based on relationships and connections between nodes in a graph. Graph mining on Facebook can be used to search for friends, places, and interests while ranking results by strength of connections. Text mining extracts meaningful information from unstructured text data on social networks and can automatically process emails, classify texts, and potentially extract full information from websites.
Analysis, modelling and protection of online private data.Silvia Puglisi
Do we have online privacy? And what is privacy anyway in an online context?
This work aim at discovering what footprints users leave online and how these represent a threat to privacy.
This document discusses data mining in social networks. It covers topics like social network analysis, graph mining, and text mining on social media platforms. Graph mining is used to understand relationships and extract communities from social networks. Text mining techniques like clustering and anomaly detection are applied to textual data from blogs, messages, etc. on social platforms. The document also discusses accessing Facebook data through its API and SDK, and applications and limitations of social network analysis.
Travel Recommendation Approach using Collaboration Filter in Social NetworkingIRJET Journal
This document discusses a travel recommendation approach using collaboration filtering in social networks. It proposes personalized travel sequence recommendations based on travelogues and community photos posted on social media. Unlike other travel recommendation systems, it recommends a sequence of points of interest rather than individual locations. It maps user preferences and route descriptions to topic categories to calculate similarity and recommend routes. The system was evaluated on a dataset of over 7 million Flickr photos and 24,000 travelogues covering 864 travel locations in 9 cities.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Team of Rivals: UX, SEO, Content & Dev UXDC 2015Marianne Sweeny
The search engine landscape has changed dramatically and now relies heavily on user experience signals to influence rank in search results. In this presentation, I explore search engine methods for evaluating UX in a machine readable fashion and present a framework for successful cross-discipline collaboration.
The document discusses the concept of personalized web search (PWS). It notes that generic web search engines cannot identify different user needs, so PWS was introduced to personalize search results. Various techniques for PWS are discussed, including user profiling using demographic and interest data, hyperlink analysis, and community-based or location-based approaches. Maintaining accurate user profiles that respect privacy is also addressed. Potential applications and limitations of PWS are mentioned.
In this world of information technology, everyone has the tendency to do business electronically. Today
lot of businesses are happening on World Wide Web (WWW), it is very important for the website owner to
provide a better platform to attract more customers for their site. Providing information in a better way is
the solution to bring more customers or users. Customer is the end-user, who accessing the information
in a way it yields some credit to the web site owners. In this paper we define web mining and present a
method to utilize web mining in a better way to know the users and website behaviour which in turn
enhance the web site information to attract more users. This paper also presents an overview of the
various researches done on pattern extraction, web content mining and how it can be taken as a catalyst
for E-business.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
1) The document discusses the evolution of search engines and algorithms over time from early concepts like Hilltop and PageRank to more modern techniques like RankBrain that use neural networks.
2) It also examines how search engines have incorporated personalization and contextualization by using implicit and explicit user data and feedback to better understand search intent and tailor results.
3) Several studies summarized found that most users expect to find information within the first 2 minutes of searching, spend little time viewing individual results, and refine queries through an iterative process as understanding develops.
This document presents an algorithm for interactively learning monotone Boolean functions. The algorithm is based on Hansel's lemma, which states that algorithms based on finding maximal upper zeros and minimal lower units are optimal for learning monotone Boolean functions. The algorithm allows decreasing the number of queries needed to learn non-monotone functions that can be represented as combinations of monotone functions. The effectiveness of the approach is demonstrated through computational experiments in engineering and medical applications.
Word accessible - .:: NIB | National Industries for the Blind ::.butest
The document provides an agenda and information for the 2009 NIB/NAEPB Annual Training Conference held in Kansas City, Missouri from October 21-24, 2009. The conference focused on technology and strategic impacts with sessions addressing business topics to help agency leaders and employees. Activities included keynote speakers, committee meetings, tours of Alphapointe Association for the Blind, and an awards banquet honoring employee award winners. Presentations and materials from the conference were made available online.
The AgentMatcher system matches learners and learning objects (LOs) using a tree-structured representation of metadata. It extracts metadata from LOs using LOMGen and stores it in a database. Learners can enter query parameters as a weighted tree, which is compared to LO metadata trees to find similar LOs. Top matches above a similarity threshold are returned to the learner. LOMGen semi-automatically generates metadata using keywords and allows an administrator to refine selections. This enhances precision over simple keyword searches.
The document discusses different machine learning algorithms for instance-based learning. It describes k-nearest neighbor classification which classifies new instances based on the labels of the k closest training examples. It also covers locally weighted regression which approximates the target function based on nearby training data. Radial basis function networks are discussed as another approach using localized kernel functions to provide a global approximation of the target function. Case-based reasoning is presented as using rich symbolic representations of instances and reasoning over retrieved similar past cases to solve new problems.
The document is a curriculum vitae for Colin Fyfe. It summarizes that he is currently a Personal Professor at the University of the West of Scotland, with educational qualifications including a BSc in Mathematics, MSc in Information Technology, and a PhD in neural networks. It also outlines his extensive employment history in education and research, as well as his significant research contributions and roles in academic administration and conference organization.
This course syllabus provides information on a 3 credit, introductory web design course. Students will learn basic HTML skills and how to design and publish web pages. They will use software like Dreamweaver and Photoshop to create web page layouts and graphics. The course involves weekly lectures, labs, assignments and 5 projects, including designing a 6-page website. Students are expected to have basic skills in programs like QuarkXPress, Illustrator and Photoshop prior to the course.
The document discusses machine learning techniques for finding patterns in data and using those patterns to make predictions. It covers topics like classification algorithms, decision trees, neural networks, learning as a search process, and how machine learning systems use bias to avoid overfitting training data. Examples are provided on classifying weather data to determine if a baseball game should be played, classifying iris flowers, predicting CPU performance, and diagnosing soybean diseases.
This document contains multiple choice questions from a board review exam covering genetics and dysmorphology. It includes questions about patterns of inheritance for connective tissue disorders, recurrence risks for cleft lip and palate, appropriate tests and evaluations for various clinical presentations, and diagnoses for infants with certain physical findings. The questions cover topics like chromosome analysis, birth defects, genetic counseling, and evaluating newborns with possible genetic syndromes.
MINUTES OF REGULAR BOARD OF EDUCATION MEETINGbutest
The board of education held their regular monthly meeting. They discussed facility improvements at the elementary, middle, and high schools. Issues like leaky roofs, outdated fire alarms, and asbestos removal were noted. The board approved meeting minutes, personnel changes, and vendor payments. They also discussed starting a youth bowling league, home school policies, and bond measures for facility upgrades. The next meeting will cover the bond election, broadband contract, and home school report.
This document discusses machine intelligence and machine learning. It covers topics such as behavior-based AI vs knowledge-based AI, supervised vs unsupervised learning, classification vs prediction, and decision tree induction for classification. Decision trees are built using an algorithm that selects the attribute that best splits the data at each step to create partitions. Pruning techniques are used to avoid overfitting.
The document discusses assessments of student learning outcomes for the Computer Science department at the University of Maryland. Small committees were formed to evaluate courses and projects based on criteria outlined in the learning outcomes.
For learning outcome #1, a committee reviewed student code from introductory programming courses to assess coding skills. Most students demonstrated proficiency in basic programming concepts.
For learning outcome #2, a committee evaluated students' ability to prove mathematical concepts by examining answers to an exam question. 75% of students received a rating of excellent or very good.
For learning outcome #3, a committee reviewed a programming project from CMSC 412 and found two of three projects examined demonstrated clear, well-documented code with good debugging
This document presents a system for classifying brain MRI series using decision tree learning. The system performs classification in two levels: 1) low-level features are used to classify segmented images into objects, and 2) high-level features synthesized from the low-level results are used to classify the full MRI series. Experiments classified MRI series as normal, cerebral infarction, or brain tumor with 93.1% accuracy. The two-level approach allows both low-level image features and high-level semantic relationships to be leveraged for classification.
This document summarizes a request for proposal from the Missouri Office of Administration for janitorial services in various state-owned buildings in St. Louis. It provides information on submitting proposals, including revising the return date to April 14, 2010. It outlines amendments made to sections of the RFP related to the contract period, specifications, and pricing. A pre-proposal conference was scheduled for March 30, 2010 to discuss the RFP and allow for questions.
This course covers statistical methods for data mining and machine learning, including linear regression and classification, nonparametric methods, generalized additive models, and tree-based methods. Students will complete homework assignments, a midterm exam, and a final project analyzing a dataset of their choice. The project involves formulating a research question, applying course techniques to answer it, and presenting results in a written report. Evaluation is based on homework, exams, and the final project. The course uses the R programming environment.
New Programme Details Set up for OSS Supporting Notesbutest
The document provides guidance for completing a form to set up a new programme of study in the Oxford Student System (OSS). It includes details about programme information such as title, type, length, responsible organisational unit, start date, entry points, attendance type, and more. It also provides guidance on timelines for setting up undergraduate programmes to ensure they are available for the upcoming admissions cycle.
A Research Platform for Coevolving Agents.docbutest
This document discusses a research platform for studying coevolving agents that interact in a producer/consumer economic world. The platform allows agents to evolve using evolutionary computation techniques. The motivations for using evolutionary computation to enable agent adaptation are discussed, including empirical evidence that complex cooperative behaviors can emerge from coevolved rulesets. Additionally, Holland's work on adaptation in natural systems provides theoretical justification for using evolutionary computation to propagate advantageous features through a distributed system of agents.
MoI_Blue_Three Ideas on Entertaining in a Presentation_2015Martin Barnes
The document discusses presenting ideas to clients in an entertaining way that generates joint project ownership. It recommends framing the idea handover moment as a highlight, treating people as fans rather than clients, and making the meeting something people look forward to and can easily share compared to the rest of their day. By clearly framing and handing over ownership of the idea in an entertaining manner, the idea can grow fast as people understand and get excited about its potential.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
AN INTELLIGENT OPTIMAL GENETIC MODEL TO INVESTIGATE THE USER USAGE BEHAVIOUR ...ijdkp
The unexpected wide spread use of WWW and dynamically increasing nature of the web creates new
challenges in the web mining since the data in the web inherently unlabelled, incomplete, non linear, and
heterogeneous. The investigation of user usage behaviour on WWW is real time problem which involves
multiple conflicting measures of performance. These measures make not only computational intensive but
also needs to the possibility of be unable to find the exact solution. Unfortunately, the conventional methods
are limited to optimization problems due to the absence of semantic certainty and presence of human
intervention. In handling such data and overcome the limitations of conventional methodologies it is
necessary to use a soft computing model that can work intelligently to attain optimal solution.
Performance of Real Time Web Traffic Analysis Using Feed Forward Neural Netw...IOSR Journals
This document discusses using feed forward neural networks and K-means clustering to analyze real-time web traffic. It proposes a technique to enhance the learning capabilities and reduce the computation intensity of a competitive learning multi-layered neural network using the K-means clustering algorithm. The model uses a multi-layered network architecture with backpropagation learning to discover and analyze knowledge from web log data. It also discusses preprocessing the web log data through cleaning, user identification, filtering, session identification and transaction identification before applying the neural network and K-means algorithms.
Enactment of Firefly Algorithm and Fuzzy C-Means Clustering For Consumer Requ...IRJET Journal
The document proposes a novel methodology for predicting consumer demand and future requests on web pages using a hybrid approach. It first classifies consumers as potential or non-potential using a firefly-based neural network with Levenberg-Marquardt algorithm. Potential consumer data is then clustered using an improved fuzzy C-means clustering algorithm. Finally, upcoming consumer demand is predicted by analyzing patterns and recommending web pages with higher weights. The proposed approach is implemented in Java and CloudSim and aims to overcome limitations of existing recommendation systems by providing more accurate and efficient predictions in shorter time.
An effective search on web log from most popular downloaded contentijdpsjournal
A Web page recommender system effectively predicts the best related web page to search. While search
ing
a word from search engine it may display some unnecessary links and unrelated data’s to user so to a
void
this problem, the con
ceptual prediction model combines both the web usage and domain knowledge. The
proposed conceptual prediction model automatically generates a semantic network of the semantic Web
usage knowledge, which is the integration of domain knowledge and web usage i
nformation. Web usage
mining aims to discover interesting and frequent user access patterns from web browsing data. The
discovered knowledge can then be used for many practical web applications such as web
recommendations, adaptive web sites, and personali
zed web search and surfing
Advance Clustering Technique Based on Markov Chain for Predicting Next User M...idescitation
According to the survey India is one of the
leading countries in the word for technical education and
management education. Numbers of students are increasing
day by day by the growth rate of 45% per annum. Advancement
in technology puts special effect on education system. This
helps in upgrading higher education. Some universities and
colleges are using these technologies. Weblog is one of them.
Main aim of this paper is to represent web logs using clustering
technique for predicting next user movement and user
behavior analysis. This paper moves around the web log
clustering technique based on Markov chain results .In this
paper we present an ideal approach to web clustering
(clustering web site users) and predicting their behavior for
next visit. Methodology: For generating effective result approx
14 engineering college web usage data is used and an advance
clustering approach is presenting after optimizing the other
clustering approach.Results: The user behavior is predicted
with the help of the advance clustering approach based on the
FPCM and k-mean. Proposed algorithm is used to mined and
predict user’s preferred paths. To predict the user behavior
existing approaches have been used. But the existing
approaches are not enough because of its reaction towards
noise. Thus with the help of ACM, noise is reduced, provides
more accurate result for predicting the user behavior. Approach
Implementation:The algorithm was implemented in MAT
LAB, DTRG and in Java .The experiment result proves that
this method is very effective in predicting user behavior. The
experimental results have validated the method’s effectiveness
in comparison with some previous studies.
International conference On Computer Science And technologyanchalsinghdm
ICGCET 2019 | 5th International Conference on Green Computing and Engineering Technologies. The conference will be held on 7th September - 9th September 2019 in Morocco. International Conference On Engineering Technology
The conference aims to promote the work of researchers, scientists, engineers and students from across the world on advancement in electronic and computer systems.
Intelligent Web Crawling (WI-IAT 2013 Tutorial)Denis Shestakov
<<< Slides can be found at http://www.slideshare.net/denshe/intelligent-crawling-shestakovwiiat13 >>>
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Web crawling, a process of collecting web pages in
an automated manner, is the primary and ubiquitous operation used by a large number of web systems and agents starting from a simple program for website backup to a major web search engine. Due to an astronomical amount of data already published on the Web and ongoing exponential growth of web content, any party that want to take advantage of massive-scale web data faces a high barrier to entry. We start with background on web crawling and the structure of the Web. We then discuss different crawling strategies and describe adaptive web crawling techniques leading to better overall crawl performance. We finally overview some of the challenges in web crawling by presenting such topics as collaborative web crawling, crawling the deep Web and crawling multimedia content. Our goals are to introduce the intelligent systems community to the challenges in web crawling research, present intelligent web crawling approaches, and engage researchers and practitioners for open issues and research problems. Our presentation could be of interest to web intelligence and intelligent agent technology communities as it particularly focuses on the usage of intelligent/adaptive techniques in the web crawling domain.
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This document proposes a new technique to enhance the learning capabilities and reduce the computation intensity of a competitive learning multi-layered neural network using the K-means clustering algorithm. The proposed model uses a multi-layered network architecture with backpropagation learning to analyze web log data. Data preprocessing steps like cleaning, user identification, and transaction identification are applied to prepare the enterprise proxy log data for analysis. The proposed framework aims to discover useful patterns from web log data through a combination of K-means clustering and a feedforward neural network.
This document provides a literature review on methods for predicting user future requests using web usage mining. It discusses several past studies that have used techniques like Markov models, clustering, association rules, and sequential pattern mining to build prediction models from web server log data. The studies aim to reduce user waiting times and server loads by pre-fetching frequently accessed web pages. The document reviews the advantages and disadvantages of different prediction techniques and algorithms discussed in previous research.
Abstract: In many fields, such as industry, commerce, government, and education, knowledge discovery and data
mining can be immensely valuable to the subject of Artificial Intelligence. Because of the recent increase in
demand for KDD techniques, such as those used in machine learning, databases, statistics, knowledge acquisition,
data visualisation, and high performance computing, knowledge discovery and data mining have grown in
importance. By employing standard formulas for computational correlations, we hope to create an integrated
technique that can be used to filter web world social information and find parallels between similar tastes of
diverse user information in a variety of settings
Certain Issues in Web Page Prediction, Classification and Clustering in Data ...IJAEMSJORNAL
Nowadays, data mining which is a part of web mining plays a vital role in various applications such as search engines, health care centers for extracting the individual patient details among huge database, analyzing disease based on basic criteria, education system for analyzing their performance level with other system, social networking, E-Commerce and knowledge management etc., which extract the information based on the user query. The issues are time taken to mine the target content or webpage from the search engines, space complexity and predicting the frequent webpage for the next user based on users’ behaviour.
Effective Performance of Information Retrieval on Web by Using Web Crawling dannyijwest
The document describes the EPOW (Effective Performance of Web Crawler) architecture, which aims to improve the performance of web crawlers. The EPOW crawler uses multiple downloaders in parallel and queues URLs to prioritize downloading. It analyzes downloaded pages to find new relevant URLs to add to the queue. The goal is to maximize download speed while minimizing overhead, keeping the crawled data fresh by periodically revisiting pages based on change frequency.
Web personalization using clustering of web usage dataijfcstjournal
The exponential growth in the number and the complexity of information resources and services on the Web
has made log data an indispensable resource to characterize the users for Web-based environment. It
creates information of related web data in the form of hierarchy structure through approximation. This
hierarchy structure can be used as the input for a variety of data mining tasks such as clustering,
association rule mining, sequence mining etc.
In this paper, we present an approach for personalizing web user environment dynamically when he
interacting with web by clustering of web usage data using concept hierarchy. The system is inferred from
the web server’s access logs by means of data and web usage mining techniques to extract the information
about users. The extracted knowledge is used for the purpose of offering a personalized view of the
services to users.
A Survey on: Utilizing of Different Features in Web Behavior PredictionEditor IJMTER
As the web user increases day by day, there are many websites which have a large
number of visitors at the same instant. So handing of these user required different technique. Out of
these requirements one emerging field is next page prediction, where as per the user navigation
pattern different features has been studied and predict the next page for the user. By this overall web
server response time is reduce. In this paper a detailed study of the different researcher paper has
shown, there techniques outcomes and list of features utilization such as web structure, web log, web
content.
IRJET - Re-Ranking of Google Search ResultsIRJET Journal
This document summarizes a research paper that proposes a hybrid personalized re-ranking approach to search results. It models a user's search interests using a conceptual user profile containing categories and concepts extracted from clicked results and a concept hierarchy. The user profile contains two types of documents - taxonomy documents representing general interests and viewed documents representing specific interests. A hybrid re-ranking process then semantically integrates the user's general and specific interests from their profile with search engine rankings to improve result relevance.
IJRET : International Journal of Research in Engineering and TechnologyImprov...eSAT Publishing House
This document summarizes techniques for improving web search results through web personalization. It discusses how web usage mining can be used to optimize information by monitoring user interaction histories and profiles. The proposed system aims to reduce manual user feedback by implicitly gathering preferences from behaviors like click-through rates and dwell times. It introduces an algorithm that calculates new ranking values for websites based on keyword matches and time spent on pages, and swaps ranks accordingly. This system provides personalized search results by continuously updating rankings based on implicit user interactions.
1. The document proposes techniques to improve search performance by matching schemas between structured and unstructured data sources.
2. It involves constructing schema mappings using named entities and schema structures. It also uses strategies to narrow the search space to relevant documents.
3. The techniques were shown to improve search accuracy and reduce time/space complexity compared to existing methods.
World Wide Web is a huge repository of information and there is a tremendous increase in the volume of
information daily. The number of users are also increasing day by day. To reduce users browsing time lot
of research is taken place. Web Usage Mining is a type of web mining in which mining techniques are
applied in log data to extract the behaviour of users. Clustering plays an important role in a broad range
of applications like Web analysis, CRM, marketing, medical diagnostics, computational biology, and many
others. Clustering is the grouping of similar instances or objects. The key factor for clustering is some sort
of measure that can determine whether two objects are similar or dissimilar. In this paper a novel
clustering method to partition user sessions into accurate clusters is discussed. The accuracy and various
performance measures of the proposed algorithm shows that the proposed method is a better method for
web log mining.
IRJET-Multi -Stage Smart Deep Web Crawling Systems: A ReviewIRJET Journal
This document summarizes research on multi-stage smart deep web crawling systems. It discusses challenges in efficiently locating deep web interfaces due to their large numbers and dynamic nature. It proposes a three-stage crawling framework to address these challenges. The first stage performs site-based searching to prioritize relevant sites. The second stage explores sites to efficiently search for forms. An adaptive learning algorithm selects features and constructs link rankers to prioritize relevant links for fast searching. Evaluation on real web data showed the framework achieves substantially higher harvest rates than existing approaches.
Este documento analiza el modelo de negocio de YouTube. Explica que YouTube y otros sitios de video online representan un nuevo modelo de negocio para contenidos audiovisuales debido al cambio en los hábitos de consumo causado por las nuevas tecnologías. Describe cómo YouTube aprovecha la participación de los usuarios para mejorar continuamente y atraer una audiencia diferente a la de los medios tradicionales.
The defense was successful in portraying Michael Jackson favorably to the jury in several ways:
1) They dressed Jackson in ornate costumes that conveyed images of purity, innocence, and humility.
2) Jackson was shown entering the courtroom as if on a red carpet, emphasizing his celebrity status.
3) Jackson appeared vulnerable, childlike, and in declining health during the trial, eliciting sympathy from jurors.
4) Defense attorney Tom Mesereau effectively presented a coherent narrative of Jackson as a victim and portrayed Neverland as a place of refuge, undermining the prosecution's arguments.
Michael Jackson was born in 1958 in Gary, Indiana and rose to fame in the 1960s as the lead singer of The Jackson 5, topping music charts in the 1970s. As a solo artist in the 1980s, his album Thriller broke music records. In the 1990s and 2000s, Jackson faced several legal issues related to child abuse allegations while continuing to release music. He married Lisa Marie Presley and Debbie Rowe and had two children before his death in 2009.
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...butest
This document appears to be a list of popular books from various authors. It includes over 150 book titles across many genres such as fiction, non-fiction, memoirs, and novels. The books cover a wide range of topics from politics to cooking to autobiographies.
The prosecution lost the Michael Jackson trial due to several key mistakes and weaknesses in their case:
1) The lead prosecutor, Thomas Sneddon, was too personally invested in the case against Jackson, having pursued him for over a decade without success.
2) Sneddon's opening statement was disorganized and weak, failing to effectively outline the prosecution's case.
3) The accuser's mother was not credible and damaged the prosecution's case through her erratic testimony, history of lies and con artist behavior.
4) Many prosecution witnesses were not credible due to prior lawsuits against Jackson, debts owed to him, or having been fired by him. Several witnesses even took the Fifth Amendment.
Here are three examples of public relations from around the world:
1. The UK government's "Be Clear on Cancer" campaign which aims to raise awareness of cancer symptoms and encourage early diagnosis.
2. Samsung's global brand marketing and sponsorship activities which aim to increase brand awareness and favorability of Samsung products worldwide.
3. The Brazilian government's efforts to improve its international image and relations with other countries through strategic communication and diplomacy.
The three most important functions of public relations are:
1. Media relations because the media is how most organizations reach their key audiences. Strong media relationships are crucial.
2. Writing, because written communication is at the core of public relations and how most information is
Michael Jackson Please Wait... provides biographical information about Michael Jackson including his birthdate, birthplace, parents, height, interests, idols, favorite foods, films, and more. It discusses his background, career highlights including influential albums like Thriller, and films he appeared in such as The Wiz and Moonwalker. The document contains photos and details about Jackson's life and illustrious music career.
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazzbutest
The document discusses the process of manufacturing celebrity and its negative byproducts. It argues that celebrities are rarely the best in their individual pursuits like singing, dancing, etc. but become famous due to being products of a system controlled by wealthy elites. This system stifles opportunities for worthy artists and creates feudalism. The document also asserts that manufactured celebrities should not be viewed as role models due to behaviors like drug abuse and narcissism that result from the celebrity-making process.
Michael Jackson was a child star who rose to fame with the Jackson 5 in the late 1960s and early 1970s. As a solo artist in the 1970s and 1980s, he had immense commercial success with albums like Off the Wall, Thriller, and Bad, which featured hit singles and groundbreaking music videos. However, his career and public image were plagued by controversies related to allegations of child sexual abuse in the 1990s and 2000s. He continued recording and performing but faced ongoing media scrutiny into his private life until his death in 2009.
Social Networks: Twitter Facebook SL - Slide 1butest
The document discusses using social networking tools like Twitter and Facebook in K-12 education. Twitter allows students and teachers to share short updates and can be used to give parents a window into classroom activities. Facebook allows targeted advertising that could be used to promote educational activities. Both tools could help facilitate communication between schools and communities if used properly while managing privacy and security concerns.
Facebook has over 300 million active users who log on daily, and allows brands to create public profile pages to interact with users. Pages are for brands and organizations only, while groups can be made by any user about any topic. Pages do not show admin names and have no limits on fans, while groups display admin names and are limited to 5,000 members. Content on pages should aim to provoke action from subscribers and establish a regular posting schedule using a conversational tone.
Executive Summary Hare Chevrolet is a General Motors dealership ...butest
Hare Chevrolet is a car dealership located in Noblesville, Indiana that has successfully used social media platforms like Twitter, Facebook, and YouTube to create a positive brand image. They invest significant time interacting directly with customers online to foster a sense of community rather than overtly advertising. As a result, Hare Chevrolet has built a large, engaged audience on social media and serves as a model for how brands can use online presences strategically.
Welcome to the Dougherty County Public Library's Facebook and ...butest
This document provides instructions for signing up for Facebook and Twitter accounts. It outlines the sign up process for both platforms, including filling out forms with name, email, password and other details. It describes how the platforms will then search for friends and suggest people to connect with. It also explains how to search for and follow the Dougherty County Public Library page on both Facebook and Twitter once signed up. The document concludes by thanking participants and providing a contact for any additional questions.
Paragon Software announces the release of Paragon NTFS for Mac OS X 8.0, which provides full read and write access to NTFS partitions on Macs. It is the fastest NTFS driver on the market, achieving speeds comparable to native Mac file systems. Paragon NTFS for Mac 8.0 fully supports the latest Mac OS X Snow Leopard operating system in 64-bit mode and allows easy transfer of files between Windows and Mac partitions without additional hardware or software.
This document provides compatibility information for Olympus digital products used with Macintosh OS X. It lists various digital cameras, photo printers, voice recorders, and accessories along with their connection type and any notes on compatibility. Some products require booting into OS 9.1 for software compatibility or do not support devices that need a serial port. Drivers and software are available for download from Olympus and other websites for many products to enable use with OS X.
To use printers managed by the university's Information Technology Services (ITS), students and faculty must install the ITS Remote Printing software on their Mac OS X computer. This allows them to add network printers, log in with their ITS account credentials, and print documents while being charged per page to funds in their pre-paid ITS account. The document provides step-by-step instructions for installing the software, adding a network printer, and printing to that printer from any internet connection on or off campus. It also explains the pay-in-advance printing payment system and how to check printing charges.
The document provides an overview of the Mac OS X user interface for beginners, including descriptions of the desktop, login screen, desktop elements like the dock and hard disk, and how to perform common tasks like opening files and folders. It also addresses frequently asked questions for Windows users switching to Mac OS X, such as where documents are stored, how to save or find documents, and what the equivalent of the C: drive is in Mac OS X. The document concludes with sections on file management tasks like creating and deleting folders, organizing files within applications, using Spotlight search, and an overview of the Dashboard feature.
This document provides a checklist for securing Mac OS X version 10.5, focusing on hardening the operating system, securing user accounts and administrator accounts, enabling file encryption and permissions, implementing intrusion detection, and maintaining password security. It describes the Unix infrastructure and security framework that Mac OS X is built on, leveraging open source software and following the Common Data Security Architecture model. The checklist can be used to audit a system or harden it against security threats.
This document summarizes a course on web design that was piloted in the summer of 2003. The course was a 3 credit course that met 4 times a week for lectures and labs. It covered topics such as XHTML, CSS, JavaScript, Photoshop, and building a basic website. 18 students from various majors enrolled. Student and instructor evaluations found the course to be very successful overall, though some improvements were suggested like ensuring proper software and pairing programming/non-programming students. The document also discusses implications of incorporating web design material into existing computer science curriculums.
1. Proposal for a
Thesis in the Field of
Information Technology
In Partial Fulfillment of the Requirements
For a Master of Liberal Arts Degree
Harvard University
Extension School
10/18/2004
Clifford Lyon
53 West Emerson Street
Melrose, MA 02176-3109
(617) 225-3293
(781) 663-7703
clyon928@comcast.net
Proposed Start Date: 10/4/2004
Anticipated Date of Graduation: 6/2005
Thesis Directors: Sergei Makar-Limanov and Bhiksha Raj
1
2. 1 Tentative Thesis Title:
Visualization of High-Dimensional Clickstream Data Using Java
Keywords: Clustering, Unsupervised Learning, Critic, Search, Data Visualization, Java 2D/3D,
Clickstream, Data Mining, Machine Learning
2 Abstract
Unsupervised learning holds out a promise for the discovery of objectively valid disaggregate
patterns within large clickstream data stores. Using an interactive data visualization interface and
clustering algorithms, the software designed and delivered by this project will allow the
exploration of clickstream data in a subjectively meaningful way.
3 Thesis Project Description
3.1 Background
Clickstream data accumulated by a commercial website offers site managers the potential for
objective insight into their audience unparalleled in other publishing media. Unlike their print,
television, and radio counterparts, web publishers have access to a detailed record of events
generated by their visitors. Each time a visitor requests a URL, a webserver records the request
and some information about the visitor’s browser in a log file. However, the potential for insight
remains in large part unrealized for the commercial Internet despite the availability of this
detailed behavior record, well-established machine learning algorithms, exponential growth in
processing power, and decreased memory and storage cost. This is in contrast to the evident
success of personalization and targeting efforts by sites such as Amazon.com and Netflix.com
that estimate the posterior probability of user decisions from previous behavior to present
contextually relevant recommendations. Content automation is certainly one positive outcome of
modeling behavior using clickstream data. However, it is fundamentally an application of
knowledge at the transaction level, not at the enterprise level. In contrast, this project seeks to
recognize patterns in web data at a high level, and to build an interface capable of presenting
these patterns to a non-technical (business) user in a meaningful way.
3.1.1 Challenge of Clickstream Data
A key reason for the lack of progress in the application of standard machine learning algorithms
to clickstream data lies in the nature of the data itself. In recent years, academics in market
research and applied economics have started building behavioral models using clickstream data.
The initial papers are interesting and encouraging. However, the shape of the data presents a
fundamental challenge. Unlike typical market research surveys and polls, web data is vast, noisy,
and censored. For example, the website contributing data for this project records more than one
billion events each month. The interesting events are those generated by real people interacting
with the website using a web browser. Software robots making requests for content generate
noise in the system. For very different reasons, these robots traverse a website using the same
protocol and transactional processes as real people. For example, a robot might gather
2
3. information for use in a search index, cache pages for a proxy server, or artificially increase
popularity for a particular product featured on a site by repeatedly requesting information. The
noise is not easily separable from interesting events. This is partly because it is easy for a robot
to do everything a real person would do, and partly because a person who configures his or her
browser to interact as minimally as possible with the webserver may appear to be a robot.
Moreover, a proxy server will appear as a single user, but in reality may convey requests for
thousands of users. Typically, a time series known as a session stores the sequence of events
generated by a user during a site visit. Analyzing time series data can help separate robot
generated events from real traffic. However, sessions are censored in the sense that there is no
event signifying completion. In particular, because the start of an event marks the end of the
previous event in the series, the final event has an unknown duration. These factors make it
difficult to model user-website interaction using raw clickstream data.
Recent efforts to apply market research techniques to clickstream data have generally used a
regression model to expose some sort of interesting user behavior pattern. Typically, websites
have extensive reporting at an aggregate level, but little real insight into user segments or the
differences between these segments. A recurring theme in recent research is the need to model
behavior in a disaggregate way to account for and expose behavior away from the mean. In
particular, Bucklin and Sismeiro (2003) suggest that accounting for user heterogeneity is of
critical importance, and that using aggregate metrics can potentially lead to the wrong
conclusions.
3.2 Approach
We propose inverting the general approach to the user behavior problem: rather than build
vectors of user or visit behavior, we will build vectors of pages, with features derived from user
or visit behavior. Metric design will account for user heterogeneity by incorporating aggregate
metrics from user dimensions as features. For example, the duration of time spent on the page
might vary depending on the time of day or the position of the page in the session. Representing
duration at a disaggregate level ensures the preservation of variance that allows users, and so
pages, to be successfully partitioned. The utility of an inverted approach is two-fold: first, we
express results in terms of website entities, which are under a site manager’s control. A low
repeat-visit rate, while important information for a site manager to know, suggests no direct
action. On the other hand, a site manager who learns that certain categories of pages are less
likely to generate repeat visits has a clear area on which to focus efforts to improve the site.
Second, using the data produced by our learning exercise as extra input for an existing user based
model may improve its predictive power. By modeling pages using unsupervised learning first,
we remove the bias of categorical features established by the top-down human design of the site.
When looking at behavior on a website, it is important to distinguish the hierarchical site
structure from the behavior on it as much as possible. Remodeling the business-driven
categorical entity as a behavioral class structure can help create better user models. A stretch
goal for the project is to use the page classification in a user behavior model to demonstrate the
value of clustering as a way of segmenting data behaviorally and supporting heterogeneity.
The software delivered by this project will cluster pages on the website according to their natural
order in the data. Clustering groups similar pages together. For example, pages visited on the
weekend by a young audience might fall into one cluster, and pages visited at the start and end of
the workday by an older group might fall into a second cluster. The pages within each group or
cluster are more similar to each other than to pages in other groups. Thus, the ordering emerges
from the data itself, rather than from an external agent. In this sense, the order is “natural.” This
3
4. approach is termed “unsupervised learning” because there is no known target class for the input
data; the model is fit to the features of the input data. The clustering algorithm assigns each page
to a class based on features derived from user interaction on that page. A user interface (GUI)
will visualize the clusters. We hope that the framework can make the often opaque results of
unsupervised learning subjectively meaningful for the site manager, that is, someone who
understands the problem domain well, but not the specifics of the machine learning process.
Unless the results have subjective meaning to the user of the software, the results will not be
useful. The software framework should be generalizable. Although the data set for our
investigation is specific, the application should perform reasonably well on other data sets.
Application testing includes scenarios using some of the common public domain machine
learning data sets, such as the iris data set (UCI Machine Learning Repository Content Summary).
In order to present data to the user, the GUI will project high-dimensional clustered page vectors
in two or three dimensions. There are established methods for achieving such a projection, such
as using the first two or three eigenvalues. Generally, the idea is to eliminate or merge features in
a way that minimizes the introduction of error into the system as information is lost. The user
interface will allow the user to search among the clusters for items of interest. The user will act
as a critic by using the search function to establish subjective validity of a given set of clusters,
and by suggesting (weighting) a direction for more useful results. While this technique has some
precedent in machine learning literature (Duda, R, Hart, P. & Stork, D. 2001, p. 565), we are
unaware of specific applications that use search as a tool for cluster exploration.
Although one readily finds examples of Java data visualization programs on the internet, we
found none that offered the feedback mechanism proposed here. An application notable for its
approach to dimensionality reduction is the two-dimensional cluster-visualization program
produced by IBM’s Alphaworks program, which can be found at
http://www.alphaworks.ibm.com/formula/CViz
The Alphaworks program places cluster exemplars at the origin and extents of the x- and y- axes,
and plots instances based on similarity. The program translates similarity into Euclidean distance
on the plane. The x- and y-axes have no units. The exemplars at the origin and extents of the
axes triangulate the placement of clustered items in the two-dimensional space: the software
places items on the plane based on similarity to the three exemplars. This method has a few nice
properties: it is fast, it does not require a lot of extra computation, and it is visually meaningful
and intuitive. Exploring this method in three dimensions would be an interesting exercise. It
might provide a parsimonious way to scale the cluster space to a low dimensional representation.
A search for similar or related material uncovered no other papers on the topic of clustering web
pages for data visualization.
3.3 Data Description
The clustering algorithm will use data from a well-known shopping services, advice, and news
website. The site records approximately 70 million page events each day. Each time a user loads
a page, a tracking image is loaded, and the resulting log line in the server log represents a single
page event. Each page event has clickstream attributes from the webserver logline and attributes
derived from the site delivery application and site meta-data. Additionally, links into and out of
the site are tracked using an HTTP redirect. These redirects share the same attributes as the page
events.
4
5. The following table represents a sample of data available at the atomic level from the database:
Field Name Description
Unique identified for the session in which the page event occurred. (A
SESSION_ID
session is continuous activity with gaps of no more than 30 minutes.
EVENT_SEQ_NUM The sequence number of the event w/in the session
REFERRING_HOST If the data is from an external site, the hostname of the external site
Foreign key to third party demographic data based on IP address.
NETWORK_IP
Provides Country, US State, DMA, Line speed.
IP_ADDRESS Client IP Address
USER_AGENT The user agent of the browser performing the page request
EDITION The “branding” of the page.
PAGE_TYPE Identifies the template used to serve the page by the content application
PAGE_DURATION Amount of time spent on the page
TIME_SINCE_SESS_START Time elapsed since the first event of the session
IS_REG_USER Whether the client was a registered user
IS_NEW_USER Whether the client has been to the site before (cookie based)
IS_COOKIED_USER Whether the client allows cookies
The sequence number of the page w/in the session (in contrast to the
PAGE_SEQ_NUM
event_seq_num, which included redirects)
IS_LAST_PAGE Whether the event was the last page
TIMESTAMP The date and time of the page request
ANONYMOUS_ID ID based on website cookie
SITE_ID The site number of the event (40 total sites)
ONTOLOGY_NODE_ID The location of the page in the site navigational hierarchy
IS_IAB_ROBOT Whether the user agent is a known robot
IS_BEHAVIORAL_ROBOT Whether the user agent behaves like a robot
The search phrase that the user types, if any. (Includes third party sites
SEARCH_PHRASE
like Google.)
REGISTRATION_ID The id of the registered user, if any.
Table 1 Sample Data Fields
These fields are the raw material that will form the aggregate page vectors. Four entities uniquely
identify a page on the website: site, page type, ontology, and asset. “Site” is a business
dimension that groups content together at a high-level. The dataset contains tens of sites. “Page
type” is an application dimension identifying the template used to render the content. The data
contains thousands of page types. An “Ontology” node is a navigational dimension describing
the area on the site where the page lives – for example, a “door”, or a “story” page. The data
contains thousands of ontology nodes. “Asset” refers to a particular piece of content or a product
featured on a page. There are tens of thousands of assets active each day, and millions
historically. We have intentionally left Asset out of the page vector key, as this would produce
far too many instances to be useful for clustering. We expect between one and ten thousand
vectors for clustering, depending on the choice of sites.
The page vector will have the following structure:
SITE_ID, PAGE_TYPE, ONTOLOGY_NODE, derived attributes 1..n.
The derived attributes will be behavioral in nature, and computed from activity over some period:
30 or 60 days, for example. Initially, we are considering the following attributes for each page:
5
6. • Count total page views
• Count 1 page sessions (this page was the only page)
• Count 2-5 page sessions (this page occurring in)
• Count 5-10 page sessions
• Count 10+ page sessions
• Count registered user visits
• Count anonymous user visits
• Repeat visitor rate
• Average hits/day
• Average hits/weekday
• Average hits/weekend
• Average hits by hour of day, flattened
• Count session starts
• Count session stops
• Count leads (occurring in sessions with this page in it – leads are redirects to a partner
site.)
• Total time spent on the page
• Average duration
• Average duration, weekday
• Average duration, weekend
• Average duration, by hours 1-24, by Time Zone
• Average “place in session” – where this event occurs, as a fraction of all events occurring
• Anonymous and Registered user visits, for each world country (flattened)
• Anonymous and Registered user visits, for each US State
This is a starting point. As mentioned, asset is not part of the page vector, but we could preserve
attributes of the asset to qualify the metrics. For example, rather than using the product entity
6
7. itself to identify a page, we could use the product category. We expect finding an identifiable set
of attributes for the page vector will require some exploration.
3.4 Data Flow
At a logical level, there are two major application components and two sources of input into the
system. The inputs are the data and the user feedback, and the components are the machine
learning and the data visualization components. Figure 1 illustrates the flow of external data into
and through the system:
Machine Learning
External
Assign Instances Import Data
Data
Clusters
Scale for
Presentation
Data Visualization
Process User
User Present Data
Input
Figure 1 Data Flow Diagram
• External Data flows into the system as a Weka dataset, a flat set of vectors containing
page information.
• The machine-learning component applies the clustering algorithm to the vectors and
thereby classifies each instance.
• The machine-learning component projects the clusters in two or three dimensions for
presentation in a user interface.
• The data visualization component processes user feedback after the presentation.
7
8. • Depending on the feedback, the data visualization component re-presents the data, or re-
classifies and then re-presents the data.
3.5 Architecture
The diagram in Figure 2 shows three physical architectural components. The shaded elements
represent elements that do not exist today; the unshaded elements represent third-party software
or data sources. The following subsections describe each of the three components.
Pre-Processing
ClickStream Extraction Script
DataStore ARFF file
Unsupervised Learning
Cluster Engine (Driver)
Weka.core.Instances
Weka.classifiers.Evaluation
Existing Clusterer
Weka.classifiers.Clusterers
New Clusterer
Multi-Dimensional Scaling Filter
Java GUI
Weka.core.Instances Visualization/Interaction
2D/3D Projection Window
Control Panel
Figure 2 System Diagram
3.5.1 Pre-processing
The preprocessing step prepares data for use. The extraction script reads data from a database
and writes it to the proprietary Weka data format. The Java application reads the formatted data
from disk into memory. We will aggregate the atomic events in the database to the page level
record described above. We will filter out robot traffic as much as possible, using already
available flags in the database. The aggregation will discount the last event of the session for
mean duration calculations.
8
9. 3.5.1.1 Clickstream Data Store
The data store exists today in the form of a large data warehouse for an anonymous web-
publishing company. They have agreed to share data for the project on condition that we
obfuscate user or commercially identifiable information. There will be some data transformation
within the data store: a script will aggregate the individual events with the page as the key,
forming the page vector described in the data description section.
3.5.1.2 Extraction Module
The extraction script pulls data from the data store. This module is a placeholder for the process
that creates a flat file in Weka format from the database. It may be a series of scripts, or it may
be a set of actions undertaken to spool query results to disk manually and add a header. Any code
developed will be handed in for inspection, but it should be understood that evidence of
completion is the Weka data file rather than code that created it. Anyone seeking to recreate this
experiment on his or her own would have to code this module by hand; the rest would flow from
there. As such, the extraction module is formally outside the bounds of the project.
3.5.1.3 Attribute-Relation File Format (ARFF) file
The Attribute-Relation File Format (ARFF) file constitutes the boundary of the application. A
Java program using the Weka class libraries can easily read the file into an in-memory
representation for machine learning by the various Weka modules. A website describing the
Weka data file format in detail is found at
http://www.cs.waikato.ac.nz/~ml/weka/arff.html
3.5.2 Unsupervised Learning
The unsupervised learning component performs the actual clustering. It clusters instances read
from the pre-processing component and refines or redistributes clusters based on feedback from
the user through the GUI component.
3.5.2.1 Cluster Engine
The cluster engine is a driver that uses the Weka data-mining framework to read the ARFF file
into memory and exercise a clustering algorithm on that data. After performing the unsupervised
learning algorithm, it prepares data for presentation by creating low-dimensional projections of
the instances. The program appends the location in the low-dimensional space to the existing
attributes of the instance. At this point in the data path, the instance includes the original
features, the cluster identifier and any related cluster metrics such as distance from the centroid,
and the newly appended location in low-dimensional space.
3.5.2.2 Multi Dimensional Scaling (MDS) Filter
The task of this component is to take the n-dimensional feature vector from the input data and
scale it to a projection suitable for presentation, i.e. either a two- or three-dimensional vector.
The filter preserves the distance between the points in the original space as closely as possible by
minimizing an error function. The MDS filter is completely independent of the clustering, and
could be applied itself to the data. This filter will do some sort of analysis like Principle
9
10. Components Analysis (PCA) on the data (Bishop, C. 1995, Appendix E). It may be we can make
use of the existing cluster information in a clever way as another alternative – in which case this
could stay in the data path as a no-op.
3.5.2.3 New Cluster Strategies
The Weka framework allows for the easy introduction of new clustering strategies. More general
classification techniques could make use of the generic classifier container as well. The Weka
library offers several clustering choices out-of-the-box: Cobweb, Expectation Maximization,
Farthest-first, and K-means (Witten, I. & Frank, E. 2000, pp. 210-227). This is a good start but
by no means exhaustive. The application does not strictly require additional clustering strategies
to function; as such, the new strategies are candidates for scope reduction.
3.5.3 Visualization/Interaction
The visualization/interaction component is the user interface to the clustered data. This
component allows the user to view the data. It allows the user to transform or refine the clusters
through a limited set of interactions.
3.5.3.1 Java GUI
The Graphical User Interface presents the user with a two- or three-dimensional projection of the
source data, using color to represent the class membership established by clustering. Intuitively,
the intensity of the color can represent the distance from the centroid or “fuzzy” class
memberships. Additionally, GUI presents the user with a set of controls that allow non-
destructive and destructive data transformations. Non-destructive operations include standard
graphical transformations such as rotation, pan, zoom. A search capability allows a user to locate
specific instances or groups of instances in the scatter plot. Such a capability may further refine
the subjectivity of the results. A proposed destructive operation of the data allows the user to act
as a critic by demonstrating what a more appropriate result might be by “forcing” their own bias
into the model. The system will re-cluster and re-present the modified instances. If happening
interactively, this would likely happen on a random sample of the data.
3.5.3.2 Projection Window (GUI Component)
The projection window is a Java component that displays a colored scatterplot of two-
dimensional or three-dimensional data. It should handle non-destructive transformations like
rotation, pan, zoom, scale, color/grayscale toggling, selection, and indicating “interesting”
instances.
3.5.3.3 Control Panel (GUI Component)
The control panel allows a user to interact with the GUI. It will provide a menu of custom
controls fulfilling all the tasks defined for the interface.
4 Work Plan
At a high level, there are two phases to the project: building the tools, and using the tools to
explore the data.
10
11. The approach to the initial phase is to work backwards through the data path, and establish
baseline functionality. The first component completed is the last in the data path, the Java GUI
front-end. Pseudo data will functionally test the GUI. The clustering engine is scheduled next.
Finally the MDS module, which in effect bridges the two initial components, will be completed.
The exception to the rule is the projection window. The work on the projection window will
happen in two parts. The initial round of work will produce a basic visualization window using
test data; a second round of development will extend the functionality for application-specific
features.
The next phase involves experimentation. Once the first component in the data path brings in the
data, the real project is underway. In addition to trials using various combinations of features,
implementation of custom clustering strategies and incremental GUI changes happen during this
phase.
Ideally, the tools would complete by Christmas, and experimentation would start in January. The
schedule below presents a slightly less aggressive view of the timeline. Code complete happens
on 2/1/2005. Pulling in this date to 1/1/2005 is a stretch goal for the development cycle.
4.1 Assumptions, Risks and Alternatives
The original code written for the project will be in Java. The cluster engine will use the Weka
open source data-mining framework (Weka 3 - Data Mining with Open Source Machine Learning
Software in Java. 2004). The GUI will use Swing components (Geary, D. 1999). It might use the
standard Java 3D extensions (Java 3D API. 2004). If it does use the AWT-based 3D API, the
GUI will use heavyweight AWT components instead of their Swing counterparts (Geary, D.
1997). Other small scripts will extract and format data for import; these may be in SQL, Perl, or
some other language. CVS versioning software will facilitate milestone releases. The eventual
application will run client-side. It might be packaged as a “Java Web Start” application.
Risks and Alternatives:
• Unachievable Schedule – the schedule as indicated below is aggressive.
o Alternative: Use third party components in the GUI, especially for prototyping
o Alternative: Use existing clustering software only
• Personal Schedule Conflict – we’re expecting our 2nd child 4/15/2005
o Alternative: Enter into the program later. Re-negotiate graduation date.
o Alternative: Build in extension.
• No signal in target data – the experiment could fail.
o Alternative: Establish signal before undertaking the project using sample data
o Alternative: Establish validity of negative outcome; success of tool
11
12. 4.2 Preliminary Schedule
Figure 3 shows a high-level view of the schedule.
1/17/2005
Full integ, Built-in Clustering
12/20/2004 - 1/3/2005
Break
1/31/2005 2/28/2005
11/22/2004 Code Complete Exploration Ends
10/4/2004 11/1/2004 Clustering 12/12/2004 3/31/2005
Start GUI Complete Dummy Data Scaling Work ends
11/1/2004 12/1/2004 1/1/2005 2/1/2005 3/1/2005
10/4/2004 3/31/2005
Figure 3 High-level View of Schedule
Table 2 shows a detailed view of the proposed schedule:
Design
Dev
Unit test
Integ Test
Explore
Scaling/ New
Projectio Controlle Clustering Projectio Extractio Clustering
Week Java GUI n r Engine n n Algorithms
4-Oct
11-Oct
18-Oct
25-Oct
1-Nov
8-Nov
15-Nov
22-Nov
29-Nov
6-Dec
13-Dec
20-Dec
27-Dec
3-Jan
10-Jan
17-Jan
24-Jan
31-Jan
7-Feb
14-Feb
21-Feb
28-Feb
7-Mar
14-Mar
21-Mar
12
13. 28-Mar
Table 2 Detailed Schedule View
5 Glossary
Centroid A pseudo exemplar serving as the statistical center of a given class.
Clustering Clustering algorithms find groups of items that are similar. For
example, clustering could be used by an insurance company to
group customers according to income, age, types of policies
purchased and prior claims experience. It divides a data set so that
records with similar content are in the same group, and groups are
as different as possible from each other. Since the categories are
unspecified, this is sometimes referred to as unsupervised learning.
(Two Crows: Data Mining Glossary. 2001).
Unsupervised Learning As distinct from supervised learning, the classification of unlabeled
data.
Data Mining The process of automatically extracting valid, useful, previously
unknown, and ultimately comprehensible information from large
databases and using it to make crucial business decisions.
“Torturing the data until they confess” (Hsu, W. 2001)
Weka An open source Java project for machine learning and data mining
found at: http://www.cs.waikato.ac.nz/~ml/weka/
Webserver A software application for serving content to browsers on the world
wide web.
6 References
6.1 Works Cited
The following is a list of references cited in the document.
Bishop, C. (1995). Neural Networks for Pattern Recognition. New York: Oxford Press.
Bucklin, R., & Sisemiro, C. (2003). A Model of Website Browsing Behavior Estimated on
Clickstream Data. Journal of Marketing Research, XL, 249-267. Retrieved August 15, 2004,
from
http://www.anderson.ucla.edu/faculty/randy.bucklin/papers/bucklinandsismeiro2003.pdf
13
14. Duda, R, Hart, P., & Stork, D. (2001) Pattern Classification. New York: John Wiley & Sons.
Geary, D (1997) Graphic Java 1.1: Mastering the AWT. New York: Prentice Hall.
Geary, D. (1999) Graphic Java Volume II: Swing. New York: Prentice Hall.
Hsu, W. (2001). Knowledge Discovery in Databases and Data Mining. Retrieved October 17,
2004, from
http://www.kddresearch.org/Courses/Fall-2003/CIS732/Lectures/Lecture-28-20011204.pdf
Java 3D API. Retrieved October 17, 2004 from http://java.sun.com/products/java-media/3D/
Two Crows: Data Mining Glossary. (2001). Retrieved October 17, 2004, from
http://www.twocrows.com/glossary.htm#anchor311516
UCI Machine Learning Repository Content Summary. Retrieved October 16, 2004, from
http://www.ics.uci.edu/~mlearn/MLSummary.html
Weka 3 - Data Mining with Open Source Machine Learning Software in Java. (2004) Retrieved
October 17, 2004, from: http://www.cs.waikato.ac.nz/ml/weka/
Witten, I., Frank, E. (2000) Data Mining. San Diego: Academic Press.
6.2 Works Consulted
The following is a list of works consulted while researching the topic.
Anasari, A., & Mela, C. (2003). E-Customization. [Electronic Version] Journal of Marketing
Research, XL, 131-145. Retrieved August 10, 2004, from
http://faculty.fuqua.duke.edu/~mela/bio/Ansari_Mela_2003.pdf
Moe, W. & Fader, P. (2002) Capturing Evolving Visit Behavior in Clickstream Data [Electronic
Version] Retrieved August 10, 2004, from http://www-
marketing.wharton.upenn.edu/ideas/pdf/00-003.pdf
Moe, W. & Fader, P. (2003) Dynamic Purchase Behavior at e-Commerce Sites [Electronic
Version] Retrieved August 10, 2004, from:
http://www-marketing.wharton.upenn.edu/ideas/pdf/Fader/Moe-Fader%20conversion
%200303.pdf
Montgomery, A., Li, S., Srinivasan, K., & Liechty, J (2004) Modeling Online Browsing and Path
Analysis Using Clickstream Data [Electronic Version] Retrieved August 10, 2004, from
http://www.andrew.cmu.edu/user/alm3/papers/purchase%20conversion.pdf
6.3 Works To Be Consulted
The following is a list of works marked for future review.
Jain, A., Murty, M., & Flynn, P. (1999). Data Clustering: A Review. ACM Computing Surveys,
31(3). Retrieved August 15, 2004, from http://portal.acm.org/citation.cfm?id=331499.331504
14
15. Leouski, A., & Swan, R. (1997). Interactive Cluster Visualization for Information Retrieval.
Retrieved August 10, 2004, from
http://citeseer.ist.psu.edu/rd/41003322%2C82112%2C1%2C0.25%2CDownload/http
%3AqSqqSqciir.cs.umass.eduqSqinfoqSqpsfilesqSqirpubsqSqir-116.ps.gz
Procopiuc, C., Jones, M., Agarwal, P., & Murali, T. (2002) A Monte Carlo Algorithm for Fast
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August 10, 2004, from http://www.research.att.com/resources/papers/Clustering.pdf
15