The document discusses various topics related to web mining and data mining. It defines web mining as using data mining techniques to extract useful information from web data. It covers different categories of web mining including web content mining, web usage mining, and web structure mining. Popular data mining techniques for these categories are discussed such as classification, clustering, association rule mining. Other topics covered include social media mining, text mining, and applications of web mining in e-commerce.
This document discusses the development of an offline web-based billing system. The previous online-only system lacked security, data sharing between managers and branches, and offline billing capabilities. The new system aims to address these issues by providing security, efficient transactions, data sharing between managers and branches, and enabling both online and offline billing. It describes the software and hardware requirements, data flow diagrams, database tables, and system architecture to support these improvements.
Business Intelligence: A Rapidly Growing Option through Web MiningIOSR Journals
This document discusses web mining techniques for business intelligence. It begins with an introduction to web mining and its subfields of web content mining, web structure mining, and web usage mining. It then focuses on web usage mining, describing the process of preprocessing log data, discovering patterns using techniques like statistical analysis and association rule mining, and analyzing the patterns. The goal is to understand customer behavior and improve business functions like marketing through data collected from web servers, proxy servers, and clients.
Odam an optimized distributed association rule mining algorithm (synopsis)Mumbai Academisc
This document proposes ODAM, an optimized distributed association rule mining algorithm. It aims to discover rules based on higher-order associations between items in distributed textual documents that are neither vertically nor horizontally distributed, but rather a hybrid of the two. Modern organizations have geographically distributed data stored locally at each site, making centralized data mining infeasible due to high communication costs. Distributed data mining emerged to address this challenge. ODAM reduces communication costs compared to previous distributed ARM algorithms by mining patterns across distributed databases without requiring data consolidation.
This document discusses data mining and provides examples of its applications and benefits. It covers the following key points in 3 sentences:
Data mining involves discovering patterns and insights from large datasets using techniques like machine learning and analytics. It helps businesses make better decisions, understand customers, and manage risk. The document provides examples of how data mining is used in applications like online retail recommendations, healthcare, finance, and more to extract valuable insights from data.
This document discusses clickstream analysis, which involves analyzing the paths users take while browsing websites. Clickstream data records users' clicks and is useful for market research and analyzing user behavior. The document then discusses how clickstream data can be collected from server logs, analyzed to identify popular and unpopular pages, and used to improve websites and target marketing strategies. It also discusses how clickstream data combined with other data sources can enable personalization.
A Practical Approach To Data Mining Presentationmillerca2
This document provides an overview of data mining, including common uses, tools, and challenges related to system performance, security, privacy, and ethics. It discusses how data mining involves extracting patterns from data using techniques like classification, clustering, and association rule learning. Maintaining privacy and anonymity while aggregating data from multiple sources for analysis poses ethical issues. The document also offers tips for gaining access to data and navigating performance concerns when conducting data mining projects.
Business Intelligence Solution Using Search Engineankur881120
The document describes a business intelligence solution that uses a search engine to index and search web pages. It discusses using crawlers to index web pages and store them in a repository. An indexer then generates an inverted index from the repository to support keyword searches. The system architecture includes the repository, indexer, and search functionality. It also describes the database structure used to store crawled URLs, the index, and search results. The project aims to build a basic search engine to demonstrate the proposed business intelligence solution.
How do you structure your information systems to enable collaboration? Through careful planning, proper structure, and
aligned technology, serendipity can happen in large scale and massive organizational benefits can be achieved.
This document discusses the development of an offline web-based billing system. The previous online-only system lacked security, data sharing between managers and branches, and offline billing capabilities. The new system aims to address these issues by providing security, efficient transactions, data sharing between managers and branches, and enabling both online and offline billing. It describes the software and hardware requirements, data flow diagrams, database tables, and system architecture to support these improvements.
Business Intelligence: A Rapidly Growing Option through Web MiningIOSR Journals
This document discusses web mining techniques for business intelligence. It begins with an introduction to web mining and its subfields of web content mining, web structure mining, and web usage mining. It then focuses on web usage mining, describing the process of preprocessing log data, discovering patterns using techniques like statistical analysis and association rule mining, and analyzing the patterns. The goal is to understand customer behavior and improve business functions like marketing through data collected from web servers, proxy servers, and clients.
Odam an optimized distributed association rule mining algorithm (synopsis)Mumbai Academisc
This document proposes ODAM, an optimized distributed association rule mining algorithm. It aims to discover rules based on higher-order associations between items in distributed textual documents that are neither vertically nor horizontally distributed, but rather a hybrid of the two. Modern organizations have geographically distributed data stored locally at each site, making centralized data mining infeasible due to high communication costs. Distributed data mining emerged to address this challenge. ODAM reduces communication costs compared to previous distributed ARM algorithms by mining patterns across distributed databases without requiring data consolidation.
This document discusses data mining and provides examples of its applications and benefits. It covers the following key points in 3 sentences:
Data mining involves discovering patterns and insights from large datasets using techniques like machine learning and analytics. It helps businesses make better decisions, understand customers, and manage risk. The document provides examples of how data mining is used in applications like online retail recommendations, healthcare, finance, and more to extract valuable insights from data.
This document discusses clickstream analysis, which involves analyzing the paths users take while browsing websites. Clickstream data records users' clicks and is useful for market research and analyzing user behavior. The document then discusses how clickstream data can be collected from server logs, analyzed to identify popular and unpopular pages, and used to improve websites and target marketing strategies. It also discusses how clickstream data combined with other data sources can enable personalization.
A Practical Approach To Data Mining Presentationmillerca2
This document provides an overview of data mining, including common uses, tools, and challenges related to system performance, security, privacy, and ethics. It discusses how data mining involves extracting patterns from data using techniques like classification, clustering, and association rule learning. Maintaining privacy and anonymity while aggregating data from multiple sources for analysis poses ethical issues. The document also offers tips for gaining access to data and navigating performance concerns when conducting data mining projects.
Business Intelligence Solution Using Search Engineankur881120
The document describes a business intelligence solution that uses a search engine to index and search web pages. It discusses using crawlers to index web pages and store them in a repository. An indexer then generates an inverted index from the repository to support keyword searches. The system architecture includes the repository, indexer, and search functionality. It also describes the database structure used to store crawled URLs, the index, and search results. The project aims to build a basic search engine to demonstrate the proposed business intelligence solution.
How do you structure your information systems to enable collaboration? Through careful planning, proper structure, and
aligned technology, serendipity can happen in large scale and massive organizational benefits can be achieved.
The document discusses various internet marketing technologies used in e-commerce. It describes features of e-commerce technology that impact marketing like ubiquity, global reach, richness, interactivity and personalization. It also discusses how web transaction logs, cookies, and other tracking files can be used to gather customer data. Other topics covered include databases, data mining, big data analytics using Hadoop, marketing automation, CRM systems, online marketing metrics, and costs of online advertising.
The document discusses web mining, which involves applying data mining techniques to discover useful information and patterns from web data. It covers the types of web data, various applications of web mining, challenges, and different techniques used. These include classification, clustering, association rule mining. It also discusses how web mining can be used to solve search engine problems and how cloud computing provides a new approach for web mining through software as a service.
ANALYTICAL IMPLEMENTATION OF WEB STRUCTURE MINING USING DATA ANALYSIS IN ONLI...IAEME Publication
Web structure mining analyzes the hyperlink structure of websites to extract useful information. It involves discovering patterns in how webpages link to each other. This can help determine the importance or relevance of individual pages. The document discusses web structure mining techniques for analyzing link patterns and relationships between webpages in order to classify pages, identify clusters of related pages, and determine the strength or type of connections between pages. It focuses on using these techniques for online booking domains.
Web content mining mines content from websites like text, images, audio, video and metadata to extract useful information. It examines both the content of websites as well as search results. Web content mining helps understand customer behavior, evaluate website performance, and boost business through research. It can classify content into categories like web page content mining and search result mining.
Web content mining mines data from web pages including text, images, audio, video, metadata and hyperlinks. It examines the content of web pages and search results to extract useful information. Web content mining helps understand customer behavior, evaluate website performance, and boost business through research. It can classify data into structured, unstructured, semi-structured and multimedia types and applies techniques such as information extraction, topic tracking, summarization, categorization and clustering to analyze the data.
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.
The International Journal of Engineering and Science (The IJES)theijes
The document provides an overview of various web content mining tools. It begins with an introduction to web mining, distinguishing between web structure mining, web content mining, and web usage mining. It then discusses web content mining in more detail. The document proceeds to describe several specific web content mining tools - Screen-scraper, Automation Anywhere 6.1, Web Info Extractor, Mozenda, and Web Content Extractor. It provides details on the features and capabilities of each tool. Finally, the document concludes by comparing the tools based on usability, ability to record data, and capability to extract structured and unstructured web data.
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
This document presents an overview of web mining techniques. It discusses how web mining uses data mining algorithms to extract useful information from the web. The document classifies web mining into three categories: web structure mining, web content mining, and web usage mining. It provides examples and explanations of techniques for each category such as document classification, clustering, association rule mining, and sequential pattern mining. The document also discusses opportunities and challenges of web mining as well as sources of web usage data like server logs.
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.
The document discusses analyzing clickstream data to understand customer behavior on e-commerce websites. It aims to identify factors that influence customers to abandon items in their carts without purchasing. The objectives are to analyze best selling products, understand browsing patterns, assess product availability, and identify customer buying trends. Feature selection and k-means clustering will be used to analyze the clickstream data and gain insights. The analysis seeks to improve the business by optimizing the customer and product experience.
The document discusses web content mining. It covers topics such as web content data structure including unstructured, semi-structured, and structured data. It also discusses techniques used for web content mining such as classification, clustering, and association. Finally, it provides examples of applications such as structured data extraction, sentiment analysis of reviews, and targeted advertising.
DEVELOPING PRODUCTS UPDATE-ALERT SYSTEM FOR E-COMMERCE WEBSITES USERS USING H...ijnlc
Websites are regarded as domains of limitless information which anyone and everyone can access. The
new trend of technology has shaped the way we do and manage our businesses. Today, advancements in
Internet technology has given rise to the proliferation of e-commerce websites. This, in turn made the
activities and lifestyles of marketers/vendors, retailers and consumers (collectively regarded as users in
this paper) easier as it provides convenient platforms to sale/order items through the internet.
Unfortunately, these desirable benefits are not without drawbacks as these platforms require that the users
spend a lot of time and efforts searching for best product deals, products updates and offers on ecommerce websites. Furthermore, they need to filter and compare search results by themselves which takes
a lot of time and there are chances of ambiguous results. In this paper, we applied web crawling and
scraping methods on an e-commerce website to obtain HTML data for identifying products updates based
on the current time. These HTML data are preprocessed to extract details of the products such as name,
price, post date and time, etc. to serve as useful information for users.
DEVELOPING PRODUCTS UPDATE-ALERT SYSTEM FOR E-COMMERCE WEBSITES USERS USING ...kevig
Websites are regarded as domains of limitless information which anyone and everyone can access. The
new trend of technology has shaped the way we do and manage our businesses. Today, advancements in
Internet technology has given rise to the proliferation of e-commerce websites. This, in turn made the
activities and lifestyles of marketers/vendors, retailers and consumers (collectively regarded as users in
this paper) easier as it provides convenient platforms to sale/order items through the internet.
Unfortunately, these desirable benefits are not without drawbacks as these platforms require that the users
spend a lot of time and efforts searching for best product deals, products updates and offers on e-
commerce websites. Furthermore, they need to filter and compare search results by themselves which takes
a lot of time and there are chances of ambiguous results. In this paper, we applied web crawling and
scraping methods on an e-commerce website to obtain HTML data for identifying products updates based
on the current time. These HTML data are preprocessed to extract details of the products such as name,
price, post date and time, etc. to serve as useful information for users.
Web mining is the application of data mining techniques to extract knowledge from web data. There are three types of web mining: web usage mining analyzes server logs to learn about user behavior; web structure mining analyzes the hyperlink structure between websites; and web content mining analyzes the contents of web pages. Web mining has various applications in areas like e-commerce, advertising, search engines, and CRM to improve business decisions by understanding customer behavior and targeting customers. It allows businesses to increase sales, optimize websites, and gain marketing intelligence.
Most of what companies know is typically held
in a data warehouse – a database that collects transactions and looks at customer transaction activity over time to understand who is buying what through which channel.
This document discusses web usage mining and related processes. It begins with an introduction to web usage mining and its goal of analyzing user behavioral patterns on websites. It then covers topics like data collection and pre-processing, including cleaning, fusion, transformation, and reduction. Specific pre-processing techniques are described, such as sessionization, pageview identification, and user identification. The document also discusses data modeling and discovery of patterns, including various pattern types like decision trees, paths, groups, and associations. Finally, it covers potential applications and conclusions about web usage mining.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
The document discusses various internet marketing technologies used in e-commerce. It describes features of e-commerce technology that impact marketing like ubiquity, global reach, richness, interactivity and personalization. It also discusses how web transaction logs, cookies, and other tracking files can be used to gather customer data. Other topics covered include databases, data mining, big data analytics using Hadoop, marketing automation, CRM systems, online marketing metrics, and costs of online advertising.
The document discusses web mining, which involves applying data mining techniques to discover useful information and patterns from web data. It covers the types of web data, various applications of web mining, challenges, and different techniques used. These include classification, clustering, association rule mining. It also discusses how web mining can be used to solve search engine problems and how cloud computing provides a new approach for web mining through software as a service.
ANALYTICAL IMPLEMENTATION OF WEB STRUCTURE MINING USING DATA ANALYSIS IN ONLI...IAEME Publication
Web structure mining analyzes the hyperlink structure of websites to extract useful information. It involves discovering patterns in how webpages link to each other. This can help determine the importance or relevance of individual pages. The document discusses web structure mining techniques for analyzing link patterns and relationships between webpages in order to classify pages, identify clusters of related pages, and determine the strength or type of connections between pages. It focuses on using these techniques for online booking domains.
Web content mining mines content from websites like text, images, audio, video and metadata to extract useful information. It examines both the content of websites as well as search results. Web content mining helps understand customer behavior, evaluate website performance, and boost business through research. It can classify content into categories like web page content mining and search result mining.
Web content mining mines data from web pages including text, images, audio, video, metadata and hyperlinks. It examines the content of web pages and search results to extract useful information. Web content mining helps understand customer behavior, evaluate website performance, and boost business through research. It can classify data into structured, unstructured, semi-structured and multimedia types and applies techniques such as information extraction, topic tracking, summarization, categorization and clustering to analyze the data.
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.
The International Journal of Engineering and Science (The IJES)theijes
The document provides an overview of various web content mining tools. It begins with an introduction to web mining, distinguishing between web structure mining, web content mining, and web usage mining. It then discusses web content mining in more detail. The document proceeds to describe several specific web content mining tools - Screen-scraper, Automation Anywhere 6.1, Web Info Extractor, Mozenda, and Web Content Extractor. It provides details on the features and capabilities of each tool. Finally, the document concludes by comparing the tools based on usability, ability to record data, and capability to extract structured and unstructured web data.
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
This document presents an overview of web mining techniques. It discusses how web mining uses data mining algorithms to extract useful information from the web. The document classifies web mining into three categories: web structure mining, web content mining, and web usage mining. It provides examples and explanations of techniques for each category such as document classification, clustering, association rule mining, and sequential pattern mining. The document also discusses opportunities and challenges of web mining as well as sources of web usage data like server logs.
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.
The document discusses analyzing clickstream data to understand customer behavior on e-commerce websites. It aims to identify factors that influence customers to abandon items in their carts without purchasing. The objectives are to analyze best selling products, understand browsing patterns, assess product availability, and identify customer buying trends. Feature selection and k-means clustering will be used to analyze the clickstream data and gain insights. The analysis seeks to improve the business by optimizing the customer and product experience.
The document discusses web content mining. It covers topics such as web content data structure including unstructured, semi-structured, and structured data. It also discusses techniques used for web content mining such as classification, clustering, and association. Finally, it provides examples of applications such as structured data extraction, sentiment analysis of reviews, and targeted advertising.
DEVELOPING PRODUCTS UPDATE-ALERT SYSTEM FOR E-COMMERCE WEBSITES USERS USING H...ijnlc
Websites are regarded as domains of limitless information which anyone and everyone can access. The
new trend of technology has shaped the way we do and manage our businesses. Today, advancements in
Internet technology has given rise to the proliferation of e-commerce websites. This, in turn made the
activities and lifestyles of marketers/vendors, retailers and consumers (collectively regarded as users in
this paper) easier as it provides convenient platforms to sale/order items through the internet.
Unfortunately, these desirable benefits are not without drawbacks as these platforms require that the users
spend a lot of time and efforts searching for best product deals, products updates and offers on ecommerce websites. Furthermore, they need to filter and compare search results by themselves which takes
a lot of time and there are chances of ambiguous results. In this paper, we applied web crawling and
scraping methods on an e-commerce website to obtain HTML data for identifying products updates based
on the current time. These HTML data are preprocessed to extract details of the products such as name,
price, post date and time, etc. to serve as useful information for users.
DEVELOPING PRODUCTS UPDATE-ALERT SYSTEM FOR E-COMMERCE WEBSITES USERS USING ...kevig
Websites are regarded as domains of limitless information which anyone and everyone can access. The
new trend of technology has shaped the way we do and manage our businesses. Today, advancements in
Internet technology has given rise to the proliferation of e-commerce websites. This, in turn made the
activities and lifestyles of marketers/vendors, retailers and consumers (collectively regarded as users in
this paper) easier as it provides convenient platforms to sale/order items through the internet.
Unfortunately, these desirable benefits are not without drawbacks as these platforms require that the users
spend a lot of time and efforts searching for best product deals, products updates and offers on e-
commerce websites. Furthermore, they need to filter and compare search results by themselves which takes
a lot of time and there are chances of ambiguous results. In this paper, we applied web crawling and
scraping methods on an e-commerce website to obtain HTML data for identifying products updates based
on the current time. These HTML data are preprocessed to extract details of the products such as name,
price, post date and time, etc. to serve as useful information for users.
Web mining is the application of data mining techniques to extract knowledge from web data. There are three types of web mining: web usage mining analyzes server logs to learn about user behavior; web structure mining analyzes the hyperlink structure between websites; and web content mining analyzes the contents of web pages. Web mining has various applications in areas like e-commerce, advertising, search engines, and CRM to improve business decisions by understanding customer behavior and targeting customers. It allows businesses to increase sales, optimize websites, and gain marketing intelligence.
Most of what companies know is typically held
in a data warehouse – a database that collects transactions and looks at customer transaction activity over time to understand who is buying what through which channel.
This document discusses web usage mining and related processes. It begins with an introduction to web usage mining and its goal of analyzing user behavioral patterns on websites. It then covers topics like data collection and pre-processing, including cleaning, fusion, transformation, and reduction. Specific pre-processing techniques are described, such as sessionization, pageview identification, and user identification. The document also discusses data modeling and discovery of patterns, including various pattern types like decision trees, paths, groups, and associations. Finally, it covers potential applications and conclusions about web usage mining.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
2. OUTLINE
◼Web mining
◼Data mining/Data mining techniques/ Data mining Algorithms
◼Social media mining
◼Text mining
◼Categories of web mining
Web content mining
Web Usage Mining
Web Structure Mining
https://orange.biolab.si/
3. WHAT IS WEB MINING?
Web Mining is the use of the data mining techniques to automatically discover and
extract information from web.
Web Mining can find interesting and potentially useful knowledge from web data
4. WHAT IS DATA MINING?
Data mining or knowledge discovery from data is the process of analyzing data from
different perspectives and summarizing it into useful information
Knowledge Discovery in Databases
Raw data knowledge
5. DATA MINING TECHNIQUES
Clustering
Classification
Association Rules
Correlation
Naive Bayesian
Neural Networks
Outlier detection/ Anomaly detection
Regression
Logistic Regression
The most popular data mining techniques are:
7. WHAT IS WEB DATA?
Web content –text , image, records, etc.
Web structure – hyperlinks, tags, etc.
Web usage –http log , app server logs ,etc
Intra-page structures- document level
Inter-page structures- hyperlink level
Supplemental data
Profiles
Registration information
Cookies
8. DATA MINING VS. WEB MINING
Data Mining
Data is structured and relational
Well-defined tables, columns, rows, keys, and constraints.
Web Mining
Semi-structured(HTML) and unstructured
9. EXAMPLE: ASSESSING CREDIT RISK
Situation: Person applies for a loan
Task: Should a bank approve the loan?
Note: People who have the best credit don’t need
the loans, and people with worst credit are not likely to repay.
Bank’s best customers are in the middle.
10. EXAMPLE: INSURANCE FRAUD
Insurance Fraud is the filing of a false claim to life, health, automobile, property or
other types of insurance benefits.
Insurance companies lose millions of dollars each year through fraudulent claims,
largely because they do not have a way to easily determine which claims are legitimate
and which may be fraudulent.
11. EXAMPLE: INSURANCE FRAUD
Data mining enables insurance companies to predict which insurance claims are likely
to be fraudulent.
http://www.hugin.com/solutions/fraud-detection-management/online-demonstration
12. OPPORTUNITIES & CHALLENGES
The amount of information on the Web is huge
The coverage of Web information is very wide and diverse.One can Find information
about almost anything. Information/data of almost all types exist on the Web. For
example, structured tables, texts, stream data, etc.
Much of the Web information is semi-structured due to the nested structure of HTML
code.
Much of the Web information is linked. There are hyperlinks among pages within a
site, and across different sites.
Much of the Web information is redundant. The same piece of information or its
variants may appear in many pages.
13. OPPORTUNITIES & CHALLENGES
The Web is noisy.A Webpage generally contains a mixture of many kinds of
information. For example: main contents, advertisements, navigation panels, copyright
notices, etc.
The Web is dynamic. New pages are constantly being generated. Keeping up with the
changes and monitoring the changes are important issues.
Above all, the Web is a virtual society. It is not only about data, information and
services, but also about interactions among people, organizations and automatic
systems,and communities.
14. APPLICATION OF WEB MINING IN E-COMMERECE
Customer Analyzing
Mined data help acquire new, retain existing customers, Improvement of merchant services and
profit by predicting customer online purchase behavior
◼What do the customers do?
◼What do the customers want?
◼How effectively use the web data to market products and to service the customer?
◼Whether customers are purposefully or just browsing?
◼Buying something they are familer with or something they know little about?
◼Are they shopping from home, from work or from a hotel?
15. Web personalization
According to the information from user behavior, a website can be designed and re-structured to
make it more advance and user-friendly. In addition, the image and product value of the
company is very important in satisfying customer need based on website quality.
Personalizing a website involves tailoring content based on the characteristics of each
individual user’s online behaviors.
Personalized content is often determined by user behaviors such as pages viewed, buttons
clicked and forms submitted.
APPLICATION OF WEB MINING IN E-COMMERECE
16. Product search & Recommendation
When the user searches for a product how we find the best results for the users?
Typically, a user query of a few keywords can match many products.
Through large-scale data analysis of query logs, we can create graphs between queries and products, and
between different products.
For example, the user who searches for “Verizon cell phones” might click on the Samsung SCH U940 Glyde
product, and the LG VX10000 Voyager. We now know the query is related to those two products, and the two
products have a relationship to each other since a user viewed (and perhaps considered buying) both.
APPLICATION OF WEB MINING IN E-COMMERECE
17. CATEGORIES OF WEB MINING
Web mining is divided into three categories:
1.Web Content Mining
2. Web Usage Mining
3. Web Structure Mining
18. WEB CONTENT MINING
To gather, categorize, organize and provide the best possible information available on the web to the user
requesting the information
The data may be unstructured or structured (data from a database) or semi-structured (html)
Content mining is the scanning and mining of text, pictures, video, audio and graphs of a Web page to
determine the relevance of the content to the search query
Content mining provides the results lists to search engines in order of highest relevance to the keywords in
the query
Web content mining is related to data mining and text mining Discovering useful information
from contents of Webpages
19. TEXT MINING
Text mining is the analysis of data contained in natural language text
Text mining attempts to derive meaning from the words and sentences in order to
classify documents, route messages appropriately, as well as create summaries of
content
Unstructured Data Examples: Email, Insurance Claim,
Web Pages, Technical Documents, Contracts
https://www.nytimes.com/2016/09/24/us/politics/presidential-debate-hillary-clinton-donald-trump.html?_r=0
https://www.youtube.com/watch?v=Ozo2QuCKml0
https://voyant-tools.org/
20. DATA MINING TECHNIQUES USING IN WEB CONTENT MINING
The more basic and popular data mining techniques in web content mining are:
Classification : Placing the documents into a predefined set of groups such as science articles, Political
articles, etc.
Clustering : Clustering is a technique used to group similar documents (is not done based on
predefined). As a result useful documents will not be omitted from the search results. Clustering helps the
user to easily select the topic of interest.
Summarization is used to reduce the length of the document by maintaining the main points. An
example for text Summarization is Microsoft word’s AutoSummarize
Visualization utilizes feature extraction and key term indexing to build a graphical representation.
Through visualization, documents having similarity are found out is useful to find out related topic from a
very large amount of documents. Examples: Word Cloud, Scatter Plot, Streamgraph, Tree map, Heat map,
Gantt Chart, etc.
21. WEB USAGE MINING
Web usage mining
Is used to understand the customer behavior
Focuses on the discovering of potential knowledge from browsing patterns of the users.
Can discover the knowledge in the hidden browsing patterns and analyses the visiting characteristics of the
users.
The primary data source used in web usage mining is the server log-files (web-logs).
Browsing web pages by the user leaves a lot of information in the log-file.
Analyzing log-files information drives us to understand the behavior of the user
Techniques use for discovering the potential knowledge from the browsing patterns are:
Clustering
Classification
Association rule
40% of Online Shopper don't complete
their purchases
23. CLASSIFICATION
Classification is the most familiar and most popular data mining technique for web usage
mining.
Data classification is the process of organizing data into categories for its most effective and
efficient use.
Classification technique uses to segment and classify observations
Example :
People with age less than 40 and salary more than 40000, trade
on line(Demographic segmentation ) .
Blackberry was launched for users who were business people, Samsung was launched for
users who like android and like various applications for a free price, and Apple was launched for
the premium customers who want to be a part of a unique and popular niche(Behavioral
segmentation)
24. CLASSIFICATION
Classification consist of assigning a class label to a set of unclassified cases.
The goal of classification is to build a model that can be used to predict the class of records whose class
label is not Know.
25. CLASSIFICATION ALGORITHMS
The most popular classification algorithms are:
Decision trees
Logistic regression
Neural networks
k-nearest neighbors
26. DECISION TREES
◼A decision tree is a graph that uses a branching method to illustrate every possible outcome of a decision.
EXAMPLE
28. Decision Tree using Orange Data Mining
Analysing data in Orange using Decision tree.
Select file: Decision tree from Dataset Folder(On Fronter)
Exercise:
Explain the output of the Decision tree
29. CLUSTERING
◼Clustering is the process of dividing a dataset into groups such that the members of
each group are as similar as possible to one another and different groups are as
dissimilar as possible from one another
◼The most popular distance-based clustering algorithms is ‘k-means’.
31. K MEANS FOR CLUSTERING
K-Means Algorithm for Clustering
The number of car accident is
classified by population
32. CLUSTERING USING ORANGE
Select file: Clustering from Dataset Folder(On Fronter)
Select K-Means from Unsupervised Widget set.
Select MDC(Multidimensional scaling )
Unsupervised Widget set
Exercise:
Explain the output of the Clustering
to create a segmentation based only on buying behavior
https://archive.ics.uci.edu/ml/datasets/Wholesale+customers
33. ASSOCIATION RULE
Association rule finds interesting associations and correlation
relationships among large sets of data items.
Association rules show attribute value conditions that occur frequently
together in a given data set.
A typical example of association rule mining is Market Basket Analysis.
What items are frequently
bought together by customers?
34. EXAMPLE OF MARKET BASKET
Items are frequently
bought together by customers, should be
placed together in the store to maximize
sales.
35. PRODUCT OFFER & RECOMMENDATIONS
IF {milk, flour, sugar, eggs, candles} THEN {party hats, paper plates, magician}
36. Association analysis in Orange
Select file: Association Rulefrom Dataset Folder(On Fronter)
Select Data Table from Data at the Widget set.
Select Frequent Itemset from Associate
Select Association Rules from Associ
Exercise:
Explain the output of the Association
https://www.lynda.com/Business-Intelligence-tutorials/Association-analysis-
Orange/475936/529739-4.html
37. WEB STRUCTURE MINING
The structure of a Web consists of Web pages as nodes, and hyperlinks as edges
connecting between two related pages
The research at the hyperlink level is also called HYPERLINK
ANALYSIS
Web structure mining is to study the relationship between the reference pages to find useful
patterns, and improve search quality by analyzing the links between pages
Web structure Mining focuses on
Reducing irrelevant search results
Help indexing information on the web
38. Web Structure Terminology
Web-Graph: A directed graph that represent the web.
Node: Each Web page is a node of the Web-graph.
Link: Each hyperlink on the Web is a directed edge of the Web-graph.
In-degree: The in-degree of a node, p is the number of distinct links that
point to p.
Out-degree: The out-degree of a node, p is the number of distinct links
originating at p that point to other nodes.
39. Web Structure Terminology
Directed Path: A sequence of links, starting from p that can be followed to reach q.
Shortest Path: Of all the paths between nodes p and q, which has the shortest length, i.e.
number of links on it.
Diameter: The maximum of all the shortest paths between a pair of nodes p and q, for all pairs of
nodes p and q in the Web-graph (the length of the longest shortest path)
40. Hubs and authorities are ‘fans’ and ‘centers’ of a web graph
A good hub page is one that points to many good authority pages
A good authority page is one that is pointed to by many good hub pages
Hubs and Authorities
42. Google’s Page Rank
Rank of a web page depends on the rank of the web pages
pointing to it
Hyperlink analysis algorithm assigns numerical weight to a
webpage
Page Rank increases effectiveness of search engines
To Climb to The Top of Google Search
43. SOCIAL MEDIA MINING
Social media mining is the process of representing, analyzing, and extracting actionable patterns and trends
from raw social media data.
Social media mining uses a range of basic concepts from computer science, data mining, machine learning,
and statistics.
Social media mining is based on theory from social network analysis(SNA)
Data mining techniques in social media mining are:
Graph Mining
Text Mining
44. SOCIAL NETWORK ANALYSIS
Social network analysis [SNA] is the mapping and measuring of relationships and flows between
people, groups, organizations, computers, and other connected information/knowledge entities.
The nodes in the network are the people and groups while the links show relationships or flows
between the nodes.
SNA provides both a visual and a mathematical analysis of human relationships.
EXAMPLE:
Who knows whom and who shares what information
and knowledge with whom through what media.
45. GRAPH MINING
Extracting useful knowledge (patterns, outliers, etc.) from structured data that can be represented as a grap
https://neo4j.com/download/
A Graph is a set of nodes and the
relationships that connect those nodes
Nodes and Relationships contain
properties to represent data.
46. TEXT MINING
◼A social network contains a lot of data in the nodes of various forms. For example, a
social network may contain blogs, articles, messages, and etc.
◼ Common application for text mining is to aid in the automatic classification of texts.
For example, it is possible to "filter" out automatically most undesirable "junk email"
based on certain terms or words that are not likely to appear in legitimate messages
48. SUMMARY
◼ Web mining
◼ Data mining
◼ Data mining techniques
◼ Web Data
◼ Applications of web mining in E-commerce
◼ Categories of web mining
Web content mining
Text mining
Data mining
o Classification
o Clustering
o Summarization
o Visualization
Web Usage Mining
Clustering –K means algorithms
Classification – Decision Tree
Association rule –Basket Analysis
Web Structure Mining
◼ Social Media Mining
Graph Mining
Text Mining