This document presents a proposed approach for optimizing web search by incorporating user feedback to improve result rankings. The approach uses keyword analysis on the user query to initially retrieve and rank relevant web pages. It then analyzes user responses like likes/dislikes and visit counts to update the page rankings. Experimental results on sample education queries show how page rankings change as user responses increase likes for certain pages. The approach aims to provide more useful search results by better reflecting individual user preferences.
This document discusses using data mining and k-means cluster analysis to classify search engine optimization (SEO) techniques. It begins with an introduction to SEO and data mining. The paper aims to analyze various SEO techniques used by webmasters and classify them using a data mining approach. Specifically, it uses k-means cluster analysis on SEO techniques to group similar techniques together and identify those with the biggest impact on webpage ranking. The literature review covers past work analyzing SEO techniques and using data mining methods like clustering for search engine optimization.
CONTENT AND USER CLICK BASED PAGE RANKING FOR IMPROVED WEB INFORMATION RETRIEVALijcsa
Search engines today are retrieving more than a few thousand web pages for a single query, most of which
are irrelevant. Listing results according to user needs is, therefore, a very real necessity. The challenge lies
in ordering retrieved pages and presenting them to users in line with their interests. Search engines,
therefore, utilize page rank algorithms to analyze and re-rank search results according to the relevance of
the user’s query by estimating (over the web) the importance of a web page. The proposed work
investigates web page ranking methods and recently-developed improvements in web page ranking.
Further, a new content-based web page rank technique is also proposed for implementation. The proposed
technique finds out how important a particular web page is by evaluating the data a user has clicked on, as
well as the contents available on these web pages. The results demonstrate the effectiveness of the proposed
page ranking technique and its efficiency.
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.
A detail survey of page re ranking various web features and techniquesijctet
This document discusses techniques for page re-ranking on websites based on user behavior analysis. It describes how web usage mining involves analyzing web server logs to extract patterns in user behavior. Common techniques discussed for page re-ranking include Markov models, data mining approaches like clustering and association rule mining, and analyzing linked web page structures. The goal is to better understand user interests and predict future page access to improve information retrieval and optimize website design.
IDENTIFYING IMPORTANT FEATURES OF USERS TO IMPROVE PAGE RANKING ALGORITHMSIJwest
Web is a wide, various and dynamic environment in which different users publish their documents. Webmining is one of data mining applications in which web patterns are explored. Studies on web mining can be categorized into three classes: application mining, content mining and structure mining. Today, internet has found an increasing significance. Search engines are considered as an important tool to respond users’ interactions. Among algorithms which is used to find pages desired by users is page rank algorithm which ranks pages based on users’ interests. However, as being the most widely used algorithm by search engines including Google, this algorithm has proved its eligibility compared to similar algorithm, but considering growth speed of Internet and increase in using this technology, improving performance of this algorithm is considered as one of the web mining necessities. Current study emphasizes on Ant Colony algorithm and marks most visited links based on higher amount of pheromone. Results of the proposed algorithm indicate high accuracy of this method compared to previous methods. Ant Colony Algorithm as one of the swarm intelligence algorithms inspired by social behavior of ants can be effective in modeling social behavior of web users. In addition, application mining and structure mining techniques can be used simultaneously to improve page ranking performance.
This document describes a system called UProRevs that aims to personalize web search results based on a user's profile and interests.
The system works as a filter that takes the results from a normal search engine like Google and re-ranks them based on their relevance to the user's profile. It generates user profiles based on information provided during registration, and updates the profiles over time based on the user's feedback on search results.
The system calculates relevance scores for search results by comparing the keywords in each web page to those in the user's profile. Results are displayed along with their relevance scores. As the user provides feedback, their profile is updated, allowing the system to continuously improve the personalization of search
Enhanced Web Usage Mining Using Fuzzy Clustering and Collaborative Filtering ...inventionjournals
This document discusses an enhanced web usage mining system using fuzzy clustering and collaborative filtering recommendation algorithms. It aims to address challenges with existing recommender systems like producing low quality recommendations for large datasets. The system architecture uses fuzzy clustering to predict future user access based on browsing behavior. Collaborative filtering is then used to produce expected results by combining fuzzy clustering outputs with a web database. This approach aims to provide users with more relevant recommendations in a shorter time compared to other systems.
`A Survey on approaches of Web Mining in Varied Areasinventionjournals
There has been lot of research in recent years for efficient web searching. Several papers have proposed algorithm for user feedback sessions, to evaluate the performance of inferring user search goals. When the information is retrieved, user clicks on a particular URL. Based on the click rate, ranking will be done automatically, clustering the feedback sessions. Web search engines have made enormous contributions to the web and society. They make finding information on the web quick and easy. However, they are far from optimal. A major deficiency of generic search engines is that they follow the ‘‘one size fits all’’ model and are not adaptable to individual users.
This document discusses using data mining and k-means cluster analysis to classify search engine optimization (SEO) techniques. It begins with an introduction to SEO and data mining. The paper aims to analyze various SEO techniques used by webmasters and classify them using a data mining approach. Specifically, it uses k-means cluster analysis on SEO techniques to group similar techniques together and identify those with the biggest impact on webpage ranking. The literature review covers past work analyzing SEO techniques and using data mining methods like clustering for search engine optimization.
CONTENT AND USER CLICK BASED PAGE RANKING FOR IMPROVED WEB INFORMATION RETRIEVALijcsa
Search engines today are retrieving more than a few thousand web pages for a single query, most of which
are irrelevant. Listing results according to user needs is, therefore, a very real necessity. The challenge lies
in ordering retrieved pages and presenting them to users in line with their interests. Search engines,
therefore, utilize page rank algorithms to analyze and re-rank search results according to the relevance of
the user’s query by estimating (over the web) the importance of a web page. The proposed work
investigates web page ranking methods and recently-developed improvements in web page ranking.
Further, a new content-based web page rank technique is also proposed for implementation. The proposed
technique finds out how important a particular web page is by evaluating the data a user has clicked on, as
well as the contents available on these web pages. The results demonstrate the effectiveness of the proposed
page ranking technique and its efficiency.
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.
A detail survey of page re ranking various web features and techniquesijctet
This document discusses techniques for page re-ranking on websites based on user behavior analysis. It describes how web usage mining involves analyzing web server logs to extract patterns in user behavior. Common techniques discussed for page re-ranking include Markov models, data mining approaches like clustering and association rule mining, and analyzing linked web page structures. The goal is to better understand user interests and predict future page access to improve information retrieval and optimize website design.
IDENTIFYING IMPORTANT FEATURES OF USERS TO IMPROVE PAGE RANKING ALGORITHMSIJwest
Web is a wide, various and dynamic environment in which different users publish their documents. Webmining is one of data mining applications in which web patterns are explored. Studies on web mining can be categorized into three classes: application mining, content mining and structure mining. Today, internet has found an increasing significance. Search engines are considered as an important tool to respond users’ interactions. Among algorithms which is used to find pages desired by users is page rank algorithm which ranks pages based on users’ interests. However, as being the most widely used algorithm by search engines including Google, this algorithm has proved its eligibility compared to similar algorithm, but considering growth speed of Internet and increase in using this technology, improving performance of this algorithm is considered as one of the web mining necessities. Current study emphasizes on Ant Colony algorithm and marks most visited links based on higher amount of pheromone. Results of the proposed algorithm indicate high accuracy of this method compared to previous methods. Ant Colony Algorithm as one of the swarm intelligence algorithms inspired by social behavior of ants can be effective in modeling social behavior of web users. In addition, application mining and structure mining techniques can be used simultaneously to improve page ranking performance.
This document describes a system called UProRevs that aims to personalize web search results based on a user's profile and interests.
The system works as a filter that takes the results from a normal search engine like Google and re-ranks them based on their relevance to the user's profile. It generates user profiles based on information provided during registration, and updates the profiles over time based on the user's feedback on search results.
The system calculates relevance scores for search results by comparing the keywords in each web page to those in the user's profile. Results are displayed along with their relevance scores. As the user provides feedback, their profile is updated, allowing the system to continuously improve the personalization of search
Enhanced Web Usage Mining Using Fuzzy Clustering and Collaborative Filtering ...inventionjournals
This document discusses an enhanced web usage mining system using fuzzy clustering and collaborative filtering recommendation algorithms. It aims to address challenges with existing recommender systems like producing low quality recommendations for large datasets. The system architecture uses fuzzy clustering to predict future user access based on browsing behavior. Collaborative filtering is then used to produce expected results by combining fuzzy clustering outputs with a web database. This approach aims to provide users with more relevant recommendations in a shorter time compared to other systems.
`A Survey on approaches of Web Mining in Varied Areasinventionjournals
There has been lot of research in recent years for efficient web searching. Several papers have proposed algorithm for user feedback sessions, to evaluate the performance of inferring user search goals. When the information is retrieved, user clicks on a particular URL. Based on the click rate, ranking will be done automatically, clustering the feedback sessions. Web search engines have made enormous contributions to the web and society. They make finding information on the web quick and easy. However, they are far from optimal. A major deficiency of generic search engines is that they follow the ‘‘one size fits all’’ model and are not adaptable to individual users.
Web Page Recommendation Using Web MiningIJERA Editor
On World Wide Web various kind of content are generated in huge amount, so to give relevant result to user web recommendation become important part of web application. On web different kind of web recommendation are made available to user every day that includes Image, Video, Audio, query suggestion and web page. In this paper we are aiming at providing framework for web page recommendation. 1) First we describe the basics of web mining, types of web mining. 2) Details of each web mining technique.3)We propose the architecture for the personalized web page recommendation.
Multi Similarity Measure based Result Merging Strategies in Meta Search EngineIDES Editor
In Meta Search Engine result merging is the key
component. Meta Search Engines provide a uniform query
interface for Internet users to search for information.
Depending on users’ needs, they select relevant sources and
map user queries into the target search engines, subsequently
merging the results. The effectiveness of a Meta Search
Engine is closely related to the result merging algorithm it
employs. In this paper, we have proposed a Meta Search
Engine, which has two distinct steps (1) searching through
surface and deep search engine, and (2) Ranking the results
through the designed ranking algorithm. Initially, the query
given by the user is inputted to the deep and surface search
engine. The proposed method used two distinct algorithms
for ranking the search results, concept similarity based
method and cosine similarity based method. Once the results
from various search engines are ranked, the proposed Meta
Search Engine merges them into a single ranked list. Finally,
the experimentation will be done to prove the efficiency of
the proposed visible and invisible web-based Meta Search
Engine in merging the relevant pages. TSAP is used as the
evaluation criteria and the algorithms are evaluated based on
these criteria.
An Effective Approach for Document Crawling With Usage Pattern and Image Base...Editor IJCATR
As the Web continues to grow day by day each and every second a new page gets uploaded into the web; it has become
a difficult task for a user to search for the relevant and necessary information using traditional retrieval approaches. The amount of
information has increased in World Wide Web, it has become difficult to get access to desired information on Web; therefore it
has become a necessity to use Information retrieval tools like Search Engines to search for desired information on the Internet or
Web. Already Existing and used Crawling, Indexing and Page Ranking techniques that are used by the underlying Search Engines
before the result gets generated, the result sets that are returned by the engine lack in accuracy, efficiency and preciseness. The
return set of result does not really satisfy the request of the user and results in frustration on the user’s side. A Large number of
irrelevant links/pages get fetched, unwanted information, topic drift, and load on servers are some of the other issues that need to
be caught and rectified towards developing an efficient and a smart search engine. The main objective of this paper is to propose
or present a solution for the improvement of the existing crawling methodology that makes an attempt to reduce the amount of load
on server by taking advantage of computational software processes known as “Migrating Agents” for downloading the related
pages that are relevant to a particular topic only. The downloaded Pages are then provided a unique positive number i.e. called the
page has been ranked, taking into consideration the combinational words that are synonyms and other related words, user
preferences using domain profiles and the interested field of a particular user and past knowledge of relevance of a web page that
is average amount of time spent by users. A solution is also been given in context to Image based web Crawling associating the
Digital Image Processing technique with Crawling.
A Survey on Web Page Recommendation and Data PreprocessingIJCERT
In today’s era, as we all know internet technologies are growing rapidly. Along with this, instantly, Web page recommendations are also improving. The aim of a Web page recommender system is to predict the Web page or pages, which will be visited from a given Web-page of a website. Data preprocessing is one basic and essential part of Web page recommendation. Data preprocessing consists of cleanup and constructing data to organize for extracting pattern. In this paper, we discuss and focus on Web page Recommendation and role of data preprocessing in Web page recommendation, considering how data preprocessing is related to Web page recommendation.
An Enhanced Approach for Detecting User's Behavior Applying Country-Wise Loca...IJSRD
This document discusses an enhanced approach for detecting user behavior through country-wise local search. It begins with an abstract describing the development of the web and challenges in the field. It then discusses various techniques for web mining including web usage mining, web content mining, and web structure mining. It also discusses sequential pattern mining algorithms and procedures for recommendation systems. The key contribution is proposing a new local search algorithm for country-wise search to make searching more efficient based on local results.
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
Personalized web search using browsing history and domain knowledgeRishikesh Pathak
This document proposes a framework for improving personalized web search by constructing an enhanced user profile using both the user's browsing history and domain knowledge. The enhanced user profile is used to better suggest relevant web pages to the user based on their search query. An experiment found that suggestions made using the enhanced user profile performed better than using a standard user profile alone. The framework involves modeling the user, re-ranking search results, and displaying personalized results based on the enhanced user profile.
Recommendation generation by integrating sequential pattern mining and semanticseSAT Journals
Abstract As the Internet usage keeps increasing, the number of web sites and hence the number of web pages also keeps increasing. A recommendation system can be used to provide personalized web service by suggesting the pages that are likely to be accessed in future. Most of the recommendation systems are based on association rule mining or based on keywords. Using the association rule mining the prediction rate is less as it doesn’t take into account the order of access of the web pages by the users. The recommendation systems that are key-word based provides lesser relevant results. This paper proposes a recommendation system that uses the advantages of sequential pattern mining and semantics over the association rule mining and keyword based systems respectively. Keywords: Sequential Pattern Mining, Taxonomy, Apriori-All, CS-Mine, Semantic, Clustering
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
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.
The document discusses various techniques for web crawling and focused web crawling. It describes the functions of web crawlers including web content mining, web structure mining, and web usage mining. It also discusses different types of crawlers and compares algorithms for focused crawling such as decision trees, neural networks, and naive bayes. The goal of focused crawling is to improve precision and download only relevant pages through relevancy prediction.
IRJET-A Survey on Web Personalization of Web Usage MiningIRJET Journal
S.Jagan, Dr.S.P.Rajagopalan "A Survey on Web Personalization of Web Usage Mining", International Research Journal of Engineering and Technology (IRJET),Volume 2,issue-01 Mar-2015. e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net , published by Fast Track Publications
Abstract
Now a day, World Wide Web (www) is a rich and most powerful source of information. Day by day it is becoming more complex and expanding in size to get maximum information details online. However, it is becoming more complex and critical task to retrieve exact information expected by its users. To deal with this problem one more powerful concept is personalization which is becoming more powerful now days. Personalization is a subclass of information filtering system that seek to predict the 'ratings' or 'preferences' that a user would give to an items, they had not yet considered, using a model built from the characteristics of an item (content-based approaches or collaborative filtering approaches). Web mining is an emerging field of data mining used to provide personalization on the web. It consist three major categories i.e. Web Content Mining, Web Usage Mining, and Web Structure Mining. This paper focuses on web usage mining and algorithms used for providing personalization on the web.
Identifying the Number of Visitors to improve Website Usability from Educatio...Editor IJCATR
Web usage mining deals with understanding the Visitor’s behaviour with a Website. It helps in understanding the concerns
such as present and future probability of every website user, relationship between behaviour and website usability. It has different
branches such as web content mining, web structure and web usage mining. The focus of this paper is on web mining usage patterns of
an educational institution web log data. There are three types of web related log data namely web access log, error log and proxy log
data. In this paper web access log data has been used as dataset because the web access log data is the typical source of navigational
behaviour of the website visitor. The study of web server log analysis is helpful in applying the web mining techniques.
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.
IRJET- A Novel Technique for Inferring User Search using Feedback SessionsIRJET Journal
This document proposes a novel technique to infer user search goals using feedback sessions. It aims to address limitations in existing approaches like noisy search results, small numbers of clicked URLs, and lack of consideration of user feedback. The proposed approach generates feedback sessions from user click logs, pre-processes the data, extracts keywords from restructured results, re-ranks the results based on keywords and user history, and categorizes the re-ranked results using predefined categories. The technique is evaluated using Average Precision, which compares it to other clustering and classification algorithms. The goal is to improve information retrieval by better representing user search interests and needs.
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 discusses web structure mining and various algorithms used for it. It begins with an abstract describing web mining and how structure mining analyzes the hyperlink structure between documents. It then provides an overview of the different types of web mining (content, structure, usage) and describes structure mining in more detail. The document focuses on structure mining algorithms like PageRank, HITS, Weighted PageRank, Distance Rank and others. It explains how each algorithm works and its advantages/disadvantages for analyzing the link structure of a website.
Search Engine Optimization and Analytics for CSEPP Advanced Training CourseBryan Campbell
This document provides an overview of search engine optimization (SEO) including definitions, key concepts, and best practices. It defines SEO as improving website visibility in organic search results. Major points covered include:
- The top factors search engines like Google consider in rankings are speed, mobile friendliness, high quality content, and links from other relevant sites.
- On-page techniques like optimizing titles, meta descriptions and images can boost rankings.
- Engagement with social media and multimedia content creates backlinks and awareness.
- Analytics tools like Google Analytics and search console help measure SEO performance and identify issues.
The document discusses various clustering approaches including partitioning, hierarchical, density-based, grid-based, model-based, frequent pattern-based, and constraint-based methods. It focuses on partitioning methods such as k-means and k-medoids clustering. K-means clustering aims to partition objects into k clusters by minimizing total intra-cluster variance, representing each cluster by its centroid. K-medoids clustering is a more robust variant that represents each cluster by its medoid or most centrally located object. The document also covers algorithms for implementing k-means and k-medoids clustering.
Cluster analysis is used to group similar objects together and separate dissimilar objects. It has applications in understanding data patterns and reducing large datasets. The main types are partitional which divides data into non-overlapping subsets, and hierarchical which arranges clusters in a tree structure. Popular clustering algorithms include k-means, hierarchical clustering, and graph-based clustering. K-means partitions data into k clusters by minimizing distances between points and cluster centroids, but requires specifying k and is sensitive to initial centroid positions. Hierarchical clustering creates nested clusters without needing to specify the number of clusters, but has higher computational costs.
Cluster analysis is a technique used to group objects based on characteristics they possess. It involves measuring the distance or similarity between objects and grouping those that are most similar together. There are two main types: hierarchical cluster analysis, which groups objects sequentially into clusters; and nonhierarchical cluster analysis, which directly assigns objects to pre-specified clusters. The choice of method depends on factors like sample size and research objectives.
Web Page Recommendation Using Web MiningIJERA Editor
On World Wide Web various kind of content are generated in huge amount, so to give relevant result to user web recommendation become important part of web application. On web different kind of web recommendation are made available to user every day that includes Image, Video, Audio, query suggestion and web page. In this paper we are aiming at providing framework for web page recommendation. 1) First we describe the basics of web mining, types of web mining. 2) Details of each web mining technique.3)We propose the architecture for the personalized web page recommendation.
Multi Similarity Measure based Result Merging Strategies in Meta Search EngineIDES Editor
In Meta Search Engine result merging is the key
component. Meta Search Engines provide a uniform query
interface for Internet users to search for information.
Depending on users’ needs, they select relevant sources and
map user queries into the target search engines, subsequently
merging the results. The effectiveness of a Meta Search
Engine is closely related to the result merging algorithm it
employs. In this paper, we have proposed a Meta Search
Engine, which has two distinct steps (1) searching through
surface and deep search engine, and (2) Ranking the results
through the designed ranking algorithm. Initially, the query
given by the user is inputted to the deep and surface search
engine. The proposed method used two distinct algorithms
for ranking the search results, concept similarity based
method and cosine similarity based method. Once the results
from various search engines are ranked, the proposed Meta
Search Engine merges them into a single ranked list. Finally,
the experimentation will be done to prove the efficiency of
the proposed visible and invisible web-based Meta Search
Engine in merging the relevant pages. TSAP is used as the
evaluation criteria and the algorithms are evaluated based on
these criteria.
An Effective Approach for Document Crawling With Usage Pattern and Image Base...Editor IJCATR
As the Web continues to grow day by day each and every second a new page gets uploaded into the web; it has become
a difficult task for a user to search for the relevant and necessary information using traditional retrieval approaches. The amount of
information has increased in World Wide Web, it has become difficult to get access to desired information on Web; therefore it
has become a necessity to use Information retrieval tools like Search Engines to search for desired information on the Internet or
Web. Already Existing and used Crawling, Indexing and Page Ranking techniques that are used by the underlying Search Engines
before the result gets generated, the result sets that are returned by the engine lack in accuracy, efficiency and preciseness. The
return set of result does not really satisfy the request of the user and results in frustration on the user’s side. A Large number of
irrelevant links/pages get fetched, unwanted information, topic drift, and load on servers are some of the other issues that need to
be caught and rectified towards developing an efficient and a smart search engine. The main objective of this paper is to propose
or present a solution for the improvement of the existing crawling methodology that makes an attempt to reduce the amount of load
on server by taking advantage of computational software processes known as “Migrating Agents” for downloading the related
pages that are relevant to a particular topic only. The downloaded Pages are then provided a unique positive number i.e. called the
page has been ranked, taking into consideration the combinational words that are synonyms and other related words, user
preferences using domain profiles and the interested field of a particular user and past knowledge of relevance of a web page that
is average amount of time spent by users. A solution is also been given in context to Image based web Crawling associating the
Digital Image Processing technique with Crawling.
A Survey on Web Page Recommendation and Data PreprocessingIJCERT
In today’s era, as we all know internet technologies are growing rapidly. Along with this, instantly, Web page recommendations are also improving. The aim of a Web page recommender system is to predict the Web page or pages, which will be visited from a given Web-page of a website. Data preprocessing is one basic and essential part of Web page recommendation. Data preprocessing consists of cleanup and constructing data to organize for extracting pattern. In this paper, we discuss and focus on Web page Recommendation and role of data preprocessing in Web page recommendation, considering how data preprocessing is related to Web page recommendation.
An Enhanced Approach for Detecting User's Behavior Applying Country-Wise Loca...IJSRD
This document discusses an enhanced approach for detecting user behavior through country-wise local search. It begins with an abstract describing the development of the web and challenges in the field. It then discusses various techniques for web mining including web usage mining, web content mining, and web structure mining. It also discusses sequential pattern mining algorithms and procedures for recommendation systems. The key contribution is proposing a new local search algorithm for country-wise search to make searching more efficient based on local results.
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
Personalized web search using browsing history and domain knowledgeRishikesh Pathak
This document proposes a framework for improving personalized web search by constructing an enhanced user profile using both the user's browsing history and domain knowledge. The enhanced user profile is used to better suggest relevant web pages to the user based on their search query. An experiment found that suggestions made using the enhanced user profile performed better than using a standard user profile alone. The framework involves modeling the user, re-ranking search results, and displaying personalized results based on the enhanced user profile.
Recommendation generation by integrating sequential pattern mining and semanticseSAT Journals
Abstract As the Internet usage keeps increasing, the number of web sites and hence the number of web pages also keeps increasing. A recommendation system can be used to provide personalized web service by suggesting the pages that are likely to be accessed in future. Most of the recommendation systems are based on association rule mining or based on keywords. Using the association rule mining the prediction rate is less as it doesn’t take into account the order of access of the web pages by the users. The recommendation systems that are key-word based provides lesser relevant results. This paper proposes a recommendation system that uses the advantages of sequential pattern mining and semantics over the association rule mining and keyword based systems respectively. Keywords: Sequential Pattern Mining, Taxonomy, Apriori-All, CS-Mine, Semantic, Clustering
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
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.
The document discusses various techniques for web crawling and focused web crawling. It describes the functions of web crawlers including web content mining, web structure mining, and web usage mining. It also discusses different types of crawlers and compares algorithms for focused crawling such as decision trees, neural networks, and naive bayes. The goal of focused crawling is to improve precision and download only relevant pages through relevancy prediction.
IRJET-A Survey on Web Personalization of Web Usage MiningIRJET Journal
S.Jagan, Dr.S.P.Rajagopalan "A Survey on Web Personalization of Web Usage Mining", International Research Journal of Engineering and Technology (IRJET),Volume 2,issue-01 Mar-2015. e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net , published by Fast Track Publications
Abstract
Now a day, World Wide Web (www) is a rich and most powerful source of information. Day by day it is becoming more complex and expanding in size to get maximum information details online. However, it is becoming more complex and critical task to retrieve exact information expected by its users. To deal with this problem one more powerful concept is personalization which is becoming more powerful now days. Personalization is a subclass of information filtering system that seek to predict the 'ratings' or 'preferences' that a user would give to an items, they had not yet considered, using a model built from the characteristics of an item (content-based approaches or collaborative filtering approaches). Web mining is an emerging field of data mining used to provide personalization on the web. It consist three major categories i.e. Web Content Mining, Web Usage Mining, and Web Structure Mining. This paper focuses on web usage mining and algorithms used for providing personalization on the web.
Identifying the Number of Visitors to improve Website Usability from Educatio...Editor IJCATR
Web usage mining deals with understanding the Visitor’s behaviour with a Website. It helps in understanding the concerns
such as present and future probability of every website user, relationship between behaviour and website usability. It has different
branches such as web content mining, web structure and web usage mining. The focus of this paper is on web mining usage patterns of
an educational institution web log data. There are three types of web related log data namely web access log, error log and proxy log
data. In this paper web access log data has been used as dataset because the web access log data is the typical source of navigational
behaviour of the website visitor. The study of web server log analysis is helpful in applying the web mining techniques.
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.
IRJET- A Novel Technique for Inferring User Search using Feedback SessionsIRJET Journal
This document proposes a novel technique to infer user search goals using feedback sessions. It aims to address limitations in existing approaches like noisy search results, small numbers of clicked URLs, and lack of consideration of user feedback. The proposed approach generates feedback sessions from user click logs, pre-processes the data, extracts keywords from restructured results, re-ranks the results based on keywords and user history, and categorizes the re-ranked results using predefined categories. The technique is evaluated using Average Precision, which compares it to other clustering and classification algorithms. The goal is to improve information retrieval by better representing user search interests and needs.
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 discusses web structure mining and various algorithms used for it. It begins with an abstract describing web mining and how structure mining analyzes the hyperlink structure between documents. It then provides an overview of the different types of web mining (content, structure, usage) and describes structure mining in more detail. The document focuses on structure mining algorithms like PageRank, HITS, Weighted PageRank, Distance Rank and others. It explains how each algorithm works and its advantages/disadvantages for analyzing the link structure of a website.
Search Engine Optimization and Analytics for CSEPP Advanced Training CourseBryan Campbell
This document provides an overview of search engine optimization (SEO) including definitions, key concepts, and best practices. It defines SEO as improving website visibility in organic search results. Major points covered include:
- The top factors search engines like Google consider in rankings are speed, mobile friendliness, high quality content, and links from other relevant sites.
- On-page techniques like optimizing titles, meta descriptions and images can boost rankings.
- Engagement with social media and multimedia content creates backlinks and awareness.
- Analytics tools like Google Analytics and search console help measure SEO performance and identify issues.
The document discusses various clustering approaches including partitioning, hierarchical, density-based, grid-based, model-based, frequent pattern-based, and constraint-based methods. It focuses on partitioning methods such as k-means and k-medoids clustering. K-means clustering aims to partition objects into k clusters by minimizing total intra-cluster variance, representing each cluster by its centroid. K-medoids clustering is a more robust variant that represents each cluster by its medoid or most centrally located object. The document also covers algorithms for implementing k-means and k-medoids clustering.
Cluster analysis is used to group similar objects together and separate dissimilar objects. It has applications in understanding data patterns and reducing large datasets. The main types are partitional which divides data into non-overlapping subsets, and hierarchical which arranges clusters in a tree structure. Popular clustering algorithms include k-means, hierarchical clustering, and graph-based clustering. K-means partitions data into k clusters by minimizing distances between points and cluster centroids, but requires specifying k and is sensitive to initial centroid positions. Hierarchical clustering creates nested clusters without needing to specify the number of clusters, but has higher computational costs.
Cluster analysis is a technique used to group objects based on characteristics they possess. It involves measuring the distance or similarity between objects and grouping those that are most similar together. There are two main types: hierarchical cluster analysis, which groups objects sequentially into clusters; and nonhierarchical cluster analysis, which directly assigns objects to pre-specified clusters. The choice of method depends on factors like sample size and research objectives.
The document discusses different types of search engines. It describes search engines as programs that use keywords to search websites and return relevant results. It provides examples of popular search engines like Google, Yahoo, and Ask.com. It also explains different types of search engines such as crawler-based, directory-based, specialty, hybrid, and meta search engines. Finally, it discusses how to effectively use search engines through techniques like being specific, using symbols like + and -, and using Boolean searches.
The document is a chapter from a textbook on data mining written by Akannsha A. Totewar, a professor at YCCE in Nagpur, India. It provides an introduction to data mining, including definitions of data mining, the motivation and evolution of the field, common data mining tasks, and major issues in data mining such as methodology, performance, and privacy.
PageRank algorithm and its variations: A Survey reportIOSR Journals
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IRJET- Text-based Domain and Image Categorization of Google Search Engine usi...IRJET Journal
This document discusses a proposed system for categorizing search engine results using conceptual clustering. The system analyzes the content of search results to extract relevant concepts, then uses a personalized conceptual clustering algorithm to generate a decision tree of query clusters. This tree can be used to identify categories for web pages and provide topically relevant results to users. The system aims to improve on traditional ranked search results by categorizing results based on the conceptual preferences and interests of individual users.
IRJET-Deep Web Crawling Efficiently using Dynamic Focused Web CrawlerIRJET Journal
This document proposes a focused semantic web crawler to efficiently access valuable and relevant deep web content in two stages. The first stage fetches relevant websites, while the second performs a deep search within sites using cosine similarity to rank pages. Deep web content, estimated at over 500 times the size of the surface web, is difficult for search engines to index as it is dynamic. The proposed crawler aims to address this using adaptive learning and storing patterns to become more efficient at locating deep web information.
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.
This document discusses web structure mining and various algorithms used for it. It begins with an abstract describing web mining and how web structure mining analyzes the link structure between web pages.
It then provides an overview of the different categories of web mining - web content mining, web structure mining, and web usage mining. For web structure mining, it describes algorithms like PageRank, HITS, weighted PageRank and others that analyze the hyperlink structure to determine important pages.
The document focuses on web structure mining and algorithms used for it like PageRank, HITS, weighted PageRank, distance rank, weighted page content rank and others. It explains how each algorithm works and its advantages/disadvantages to analyze the web
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.
This document summarizes an approach to improve personalized ranking using associated edge weights. The key points are:
1) Personalized ranking aims to improve traditional search and retrieval by tailoring results to a user's interests. Authority flow approaches like PageRank and ObjectRank can be used for personalized ranking on entity-relationship graphs.
2) Edge-based personalization assigns different weights to edge types based on the user, affecting authority flow. The paper focuses on improving edge-based personalization by hybridizing the ScaleRank algorithm with k-means clustering.
3) ScaleRank approximates the DataApprox algorithm for ranking. The hybrid approach uses k-means clustering on ScaleRank output to further group related nodes,
Comparable Analysis of Web Mining Categoriestheijes
Web Data Mining is the current field of analysis which is a combination of two research area known as Data Mining and World Wide Web. Web Data Mining research associates with various research diversities like Database, Artificial Intelligence and Information redeem. The mining techniques are categorized into various categories namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this work, analysis of mining techniques are done. From the analysis it has been concluded that Web Content Mining has unstructured or semi- structure view of data whereas Web Structure Mining have linked structure and Web Usage Mining mainly includes interaction.
This document provides an overview of web mining, which involves applying data mining techniques to discover patterns from data on the world wide web. It begins by defining web mining and presenting a taxonomy that distinguishes between web content mining and web usage mining. Web content mining involves discovering information from web sources, while web usage mining involves analyzing user browsing patterns. The document then surveys research on pattern discovery techniques applied to web transactions, analyzing discovered patterns, and architectures for web usage mining systems. It concludes by outlining open research directions in areas like data preprocessing, the mining process, and analyzing mined knowledge.
A machine learning approach to web page filtering using ...butest
This document describes a machine learning approach to web page filtering that combines content and structural analysis. The proposed approach represents web pages with features extracted from content and links. These features are used as input for machine learning algorithms like neural networks and support vector machines to classify pages. An experiment compares this approach to keyword-based and lexicon-based filtering, finding the proposed approach generally performs better, especially with few training documents.
A machine learning approach to web page filtering using ...butest
This document describes a machine learning approach to web page filtering that combines content and structural analysis. The proposed approach represents web pages with features extracted from content, such as terms and phrases, and from links. These features are used as input for machine learning algorithms like neural networks and support vector machines to classify pages. An experiment compares this approach to keyword-based and lexicon-based filtering, finding the proposed approach generally performs better, especially with few training examples. The approach could benefit topic-specific search engines and other applications.
Farthest first clustering in links reorganizationIJwest
Website can be easily design but to efficient user navigation is not a easy task since user behavior is keep changing and developer view is quite different from what user wants, so to improve navigation one way is reorganization of website structure. For reorganization here proposed strategy is farthest first traversal clustering algorithm perform clustering on two numeric parameters and for finding frequent traversal path of user Apriori algorithm is used. Our aim is to perform reorganization with fewer changes in website structure.
Web Usage Mining: A Survey on User's Navigation Pattern from Web Logsijsrd.com
With an expontial growth of World Wide Web, there are so many information overloaded and it became hard to find out data according to need. Web usage mining is a part of web mining, which deal with automatic discovery of user navigation pattern from web log. This paper presents an overview of web mining and also provide navigation pattern from classification and clustering algorithm for web usage mining. Web usage mining contain three important task namely data preprocessing, pattern discovery and pattern analysis based on discovered pattern. And also contain the comparative study of web mining techniques.
This document provides a literature survey and comparison of different techniques for web mining, including web structure mining, web usage mining, and web content mining. It summarizes various page ranking algorithms and models like PageRank, Weighted PageRank, HITS, General Utility Mining, and Topological Frequency Utility Mining. The document compares these algorithms and models based on the type of web mining activity, whether they consider website topology, their processing approach, and limitations. It aims to help compare techniques for analyzing the structure, usage, and content of websites.
IDENTIFYING IMPORTANT FEATURES OF USERS TO IMPROVE PAGE RANKING ALGORITHMSZac Darcy
A Conversational Agent for the Web of Data, Journal of Web Semantics, Volume 37–38,
2016, Pages 64-85, ISSN 1570-8268.
[4] J. M. Kleinberg, (1999), Authoritative sources in a hyperlinked environment, Journal of the ACM
(JACM), 46(5), 604-632.
[5] L. Page, S. Brin, R. Motwani, and T. Winograd, (1999), The PageRank citation ranking: Bringing
order to the web. Technical Report, Stanford InfoLab.
[6] S. Chakrabarti, (2003), Min
Identifying Important Features of Users to Improve Page Ranking Algorithms dannyijwest
Increase in number of ontologies on Semantic Web and endorsement of OWL as language of discourse for the Semantic Web has lead to a scenario where research efforts in the field of ontology engineering may be applied for making the process of ontology development through reuse a viable option for ontology developers. The advantages are twofold as when existing ontological artefacts from the Semantic Web are reused, semantic heterogeneity is reduced and help in interoperability which is the essence of Semantic Web. From the perspective of ontology development advantages of reuse are in terms of cutting down on cost as well as development life as ontology engineering requires expert domain skills and is time taking process. We have devised a framework to address challenges associated with reusing ontologies from the Semantic Web. In this paper we present methods adopted for extraction and integration of concepts across multiple ontologies. We have based extraction method on features of OWL language constructs and context to extract concepts and for integration a relative semantic similarity measure is devised. We also present here guidelines for evaluation of ontology constructed. The proposed methods have been applied on concepts from food ontology and evaluation has been done on concepts from domain of academics using Golden Ontology Evaluation Method with satisfactory outcomes
COST-SENSITIVE TOPICAL DATA ACQUISITION FROM THE WEBIJDKP
The cost of acquiring training data instances for induction of data mining models is one of the main concerns in real-world problems. The web is a comprehensive source for many types of data which can be used for data mining tasks. But the distributed and dynamic nature of web dictates the use of solutions which can handle these characteristics. In this paper, we introduce an automatic method for topical data acquisition from the web. We propose a new type of topical crawlers that use a hybrid link context extraction method for topical crawling to acquire on-topic web pages with minimum bandwidth usage and with the lowest cost. The new link context extraction method which is called Block Text Window (BTW), combines a text window method with a block-based method and overcomes challenges of each of these methods using the advantages of the other one. Experimental results show the predominance of BTW in comparison with state of the art automatic topical web data acquisition methods based on standard metrics.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
This document describes an intelligent meta search engine that was developed to efficiently retrieve relevant web documents. The meta search engine submits user queries to multiple traditional search engines including Google, Yahoo, Bing and Ask. It then uses a crawler and modified page ranking algorithm to analyze and rank the results from the different search engines. The top results are then generated and displayed to the user, aimed to be more relevant than results from individual search engines. The meta search engine was implemented using technologies like PHP, MySQL and utilizes components like a graphical user interface, query formulator, metacrawler and redundant URL eliminator.
Similar to Data mining in web search engine optimization (20)
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
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9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
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This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
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Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Data mining in web search engine optimization
1. International Journal of Computer Applications (0975 – 8887)
Volume 95– No.8, June 2014
1
Data Mining in Web Search Engine Optimization and
User Assisted Rank Results
Minky Jindal
Institute of Technology and Management
Gurgaon 122017, Haryana, India
Nisha kharb
Institute of Technology and Management
Gurgaon 122017, Haryana, India
ABSTRACT
In the fast moving world, the use of web is been increasing
day by day so that the requirement of users relative to web
search are also increasing. The content search over the web is
one of the important research area comes under the web
content mining. According to a traditional search engine, the
search is based on the content based matching. But when
some site is optimized under the SEO tools, such kind of
search is not effective in all ways. The aim of this research is
to design a user assisted, reliable, search based on the
keyword based analysis ,to provide the user assisted ranked
results so that user can select the priority links ,discard the
spam links over the web and efficient search optimization
model over the open web. The main objective of the work is
to implement the work in user friendly environment and
analysis of work under different parameters.
Keywords
web pages, data mining, web mining, extreme programming,
software tool.
1. INTRODUCTION
1.1 Data Mining
Web usage mining is a subset of web mining operations which
itself is a subset of data mining in general. The aim is to use
the data and information extracted in web systems in order to
reach knowledge of the system itself. Data mining is different
from information extraction although they are closely related.
To better understand the concepts brief definitions of
keywords can be given as [1].
Data:- “A class of information objects, made up of
units of binary code that are intended to be stored,
processed, and transmitted by digital computers”.
Information:-“is a set of facts with processing
capability added, such as context, relationships to
other facts about the same or related objects,
implying an increased usefulness. Information
provides meaning to data”
Knowledge :- “is the summation of information
into independent concepts and rules that can explain
relationships or predict outcomes”
Information extraction is the process of extraction information
from data sources whether they are structured, unstructured or
semi-structured into structured and computer understandable
data formats. Area where data mining is widely used is
bioinformatics where very large data about protein structures,
networks and genetic material is analyzed. The sub category
of interest in this thesis is the web mining which acts on the
data made available in the World Wide Web (WWW) data
servers.
1.1.1 Web Mining
Web mining consists of a set operations defined on data
residing on WWW data servers defines web mining as“…the
discovery and analysis of useful information from the World
Wide Web”. Web mining as a sub category of data mining is
fairly recent compared to their areas since the introduction of
internet and its widespread usage itself is also recent.
However, the incentive to mine the data available on the
internet is quite strong. Both the number of users around the
world accessing online data and the volume of the data itself
motivate the stakeholders of the web sites to consider
analyzing the data and user behavior. Web mining is mainly
categorized into two subsets namely web content mining and
web usage mining. While the content mining approaches
focus on the content of single web pages, web usage mining
uses server logs that detail the past accesses to the web site
data made available to public.
1.1.2 Web Content Mining
“Web content mining describes the automatic search of
information resources available on-line”. The focus is on the
content of web pages themselves. content mining as agent-
based approaches; where intelligent web agents such as
crawlers autonomously crawl the web and classify data and
database approaches; where information retrieval tasks are
employed to store web data in databases where data mining
process can take place Most web content mining studies have
focused on textual and graphical data since the early years of
internet mostly featured textual or graphical information.
Recent studies started to focus on visual and aural data such
as sound and video content too [2,3].
1.1.3 Web Usage Mining
The main topic of this thesis is the web usage mining. Usage
mining as the name implies focus on how the users of
websites interact with web site, the web pages visited, the
order of visit, timestamps of visits and durations of them. The
main source of data for the web usage mining is the server
logs which log each visit to each web page with possibly IP,
referrer, time, browser and accessed page link. Although
many areas and applications can be cited where usage mining
is useful, it can be said the main idea behind web usage
mining is to let users of a web site to use it with ease
efficiently, predict and recommend parts of the web site to
user based on their and previous users actions on the web
site[4].
1.2 LITERATURE SURVEY
In Year 2011, D. Choi has defined an approach to perform the
query over the web and to extract the web document. The
author also presented the approach to assign the ranking to
these web documents. With the development of web search
engines, one of the major tasks is to retrieve these documents
from web effectively. These search engines uses the some
ranking algorithm to present the result in an effective way.
2. International Journal of Computer Applications (0975 – 8887)
Volume 95– No.8, June 2014
2
The author has defined a study of existing ranking algorithm
used by different search engines. The author explored the
advantages and limitations of these ranking algorithms. The
major contribution of author was the definition of query based
information retrieval. The author defined the classification
over a query and performed the query filtration. Based on this
analysis, the ranking is improved and refined [5]
Zhou Hui[6] has presented a work on optimization of search
engine under the keyword analysis along with face link
analysis and back link analysis. Author defined a relational
environment based on search engine optimization os that the
search ranking will be improved. Author also discussed
various aspects of search engine optimization including the
optimization vector, ranking, working principal etc.
Ping-Tsai Chun[7] has presented a search engine optimization
approach under the current market scenario analysis. Author
defined the web service analysis to improve the business
dictating and to provide the work under small organizations so
that the effective keyword analysis based search will be
performed. Author presented the work for text search as well
as for image search over the web. The pattern analysis is
defined to perform the effective search over web.
One of the common model for web page ranking and
prediction system is defined by Markov Model. Such model
defines the navigational behavior of web graph theory as well
as defines the transitional probabilities over the ranking
analysis. The author not only defined work for a single web
page access, but also presents the work for web path
generation. The web path is actually defined as a series of web
pages that a user can visit after visiting a specific web page.
To perform this kind of analysis a Markov Model based
prediction system is defined. The prediction is here defined
under the web usage mining that defined the structural
information for prediction of web pages. To perform such
kind of analysis, the author defined a web page graph and
implements the markov model over it to analyze the
frequency match. Based on which a acyclic web path is
generated and based on the weightage assigned to this web
path the prediction is performed [8]. Another work in web
page ranking is the comparison of different web pages and the
web sites. Author M. Klein performed this comparison on two
football team web sites of college team. The analysis is
performed under the web page metrics to perform the quality
assessment. The author has defined the page comparison and
the ranking system under the graph theory[9].
2. PROPOSED APPROACH
The proposed work is about to optimize the topic based Web
Service crawling process with the concept of exclusion of
duplicate pages. For this a new architecture is proposed, this
architecture will use the rank based service selection
approach. In this work the ranking is performed respective
main criteria’s called User Interest Analysis. The user interest
analysis is further categorized under three sub categories
called User Query Relevancy Analysis, User
Recommendation to service in terms of like dislike factor and
the user Web service visits. The basic phenomenon is given as
under.
Figure 1: Proposed Web Search Architecture
As we can see in this proposed architecture the user will
interact to the Web Service with his topic based query to
retrieve the Web Service pages. As the page is query
performed it will perform request to the Web Service and
generate the basic url list. Now it will retrieve the data from
the Web Service. For the url collection it will use some
concepts like indexing and the ranking. The indexing will
provide a fast access to the Web Service page where as
ranking will arrange the list according to the priority. Now as
a Web Service page is fetched, the proposed approach will
retrieve the keywords form the document and perform the
relevancy match by performing the match of service keywords
with user query. Now as a new page is retrieved it will
generate the suffix tree and perform a suffix tree based
comparison to analyze the relevancy ratio. Based on this
factor the initial ranking is assigned to the Web service. Here
Fig 1 is showing the proposed web architecture. The web
architecture is divided in three mains stages. At the earlier
stage, the keyword analysis is performed in terms of keyword
identification over the query and removes the stop list words
from the query. After this stage, the keyword extraction over
the query is obtained. Now this keyword list is considered as
the query and passed over the web. As the web contents are
extracted over the web, the link analysis is performed. This
analysis will obtain the web contents and perform the content
based match to obtain the most relevant pages over the web.
The steps involved proposed work is presented in the Figure
2.
Crawling the Web Page based on query
Parsing the Web Contents to Text Form
Identify the relevancy Vector and assign initial ranking
Obtain User response
Estimate ranking
Result will be displayed
Figure 2: Process Model of Proposed Work
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Volume 95– No.8, June 2014
3
3. PROPOSED ALGORITHM
Algorithm {
1. Initialize the Web Environment
2. Get user Query
3. Accept the user query and filter it to retrieve the
keywords under the following step
a. Remove the stoplist words from the query
list
b. Remove the Similar Words
c. Extract the Keywords From the Query
4. Use these extracted keywords as the main query to
the web system
5. Extract the web contents and find the occurrence of
the keywords in the web pages
6. Find the maximum match web page from the web
respective to its contents as well as internal link
contents
7. Find the list of M web pages from the web that
satisfy the relevancy vector
8. For i=1 to M
[Perform the Content based similarity measure as]
{
9. RelevencyVector = 0
For j=1 to Length (UserKeywords)
{
RelevencyVector=RelevencyVector +
KeywordOccurance(Page(i) ,Keyword(j))
/TotalKeywords(Page(i),Keyword(j));
}
10. Check the Existence of Particular web server in
Database if it does not exist then set this relevancy
vector as the initial ranking parameter
11. Obtain the rank based on user response parameters
i.e. like, dislike and
12. Update the rank based on user response.
}
13. Show the ranked list of pages to user
}
4. EXPERIMENTAL RESULTS
Here Figure 3 is showing the graphical screen on which the
user pass the query to search engine. The results are obtained
for the web user based on this query by performs server side
check under different parameters.
Figure 3: Graphical Screen
Here Figure 4 is showing the results obtained from the web
server based on user query. The query results are presented as
the base results and decide the initial ranking based on the
relevancy vector,
Figure 4: Initial Results
The given Figure 5 is showing the results based on proposed
user assisted weighted model. The ranks to links are assigned.
The primary ranking is based on relevancy vector. As the like
vector will be improved, the rank will be improved and as the
dislike vector will be increased, the rank will be decreased
4. International Journal of Computer Applications (0975 – 8887)
Volume 95– No.8, June 2014
4
Figure 5: Ranked Results
The given Figure 6 is the impact of like vector. The like clicks
on web link “education.com” is increased, ranking of the
particular link is increased.
Figure 6: Modified Ranked Results Based On Response
Here figure 7 is showing the analysis of crawled pages under
different parameters based on which the ranking to the web
pages is assigned. These parameters includes like, dislike,
visit count and ranking. Here figure is showing, As the user
response is provided to these pages, the ranking is changed.
As shown in figure, As the like vector is increased, the
ranking of particular page is also increased. In same way,
dislike vector and visit count also affect the ranking.
Figure 7: Ranked Page Analysis for Education Keyword
Here figure 8 is showing the analysis of crawled pages under
different parameters based on which the ranking to the web
pages is assigned. These parameters includes like, dislike,
visit count and ranking. Here figure is showing, As the user
response is provided to these pages, the ranking is changed.
As shown in figure, As the like vector is increased, the
ranking of particular page is also increased. In same way,
dislike vector and visit count also affect the ranking.
Figure 8: Ranked Page Analysis for Education Keyword
5. CONCLUSION
In this paper we have mainly presented work is about to
perform the effective search in the Web environment based on
the user query relevancy factor. The relevancy of the query is
here analyzed under three main factors called Keyword based
Analysis, User Recommendation Analysis and the User Web
service visit analysis. Based on these all factors a ranking
criterion is decided and based on these ranking vectors the
Web services are ordered. The user can get the best Web
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Volume 95– No.8, June 2014
5
service as well as recommend other for the best service
selection. In this work, the google App is used as the public
Web repository to perform the query analysis. The work is
implemented in a web environment to perform the user query
and to derive the ordered results from the query.
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