Web mining
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Web mining
The document discusses web mining including extracting information from web documents and services. It describes challenges of web mining like noisy and dynamic web data. It then outlines the data flow system including developing dimension tables for date, time of day, visitor, object, referrer, status, browser, and operating system. Finally, it specifies reports on statistics of visits, most/least popular pages, visitor locations, frequent visitors, top referrers/keywords, and most used browsers and operating systems.
Web mining
This document discusses web mining and its various types and applications. It defines web mining as the extraction of useful information from web documents and services. There are three main types of web mining: web content mining analyzes the content of web pages, web structure mining examines the link structure between pages, and web usage mining studies user access patterns by analyzing server logs. The challenges and pros of web mining are also covered, along with its applications in areas like e-commerce, business intelligence, and knowledge management.
Web mining
General Idea about web mining and different methods of web mining and terminologies associated with web mining and Usage of web mining, differentiation between web mining and data mining.
Web mining (1)
This document discusses web mining and its various types. Web mining involves using data mining techniques to discover useful information from web documents and usage patterns. It can involve content mining of text, images, video and audio to extract useful information. It also includes structure mining, which analyzes the hyperlink structure between documents and within documents. Additionally, web usage mining analyzes log files from web servers and applications to discover interesting usage patterns. The document outlines the differences between traditional data mining and web mining. It provides examples of applications of web mining such as information retrieval, network management and e-commerce.
Web mining
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.
Web mining
This document outlines a presentation on web mining. It begins with an introduction comparing data mining and web mining, noting that web mining extracts information from the world wide web. It then discusses the reasons for and types of web mining, including web content, structure, and usage mining. The document also covers the architecture and applications of web mining, challenges, and provides recommendations.
Web mining
This document discusses different types of web mining including web usage mining, web content mining, and web structure mining. It provides examples of web usage mining including tracking user browsing behavior and usage patterns to target customers and enhance experiences. Web structure mining aims to discover the link structure of websites and identify related pages. It describes techniques like PageRank and HITS algorithm. The document also provides a practical example of analyzing usage data according to demographics and a challenges section discussing issues around web scale and diversity.
Web Usage Pattern
This document discusses web usage mining. It begins by defining web mining and its three categories: web content mining, web structure mining, and web usage mining. The main focus is on web usage mining, which involves discovering user navigation patterns and predicting user behavior. The key processes of web usage mining are preprocessing raw data, pattern discovery using algorithms, and pattern analysis. Pattern discovery techniques discussed include statistical analysis, clustering, classification, association rules, and sequential patterns. Potential applications are personalized recommendations, system improvements, and business intelligence. The document concludes by discussing future research directions such as usage mining on the semantic web and analyzing discovered patterns.
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Web mining
The document discusses web mining including extracting information from web documents and services. It describes challenges of web mining like noisy and dynamic web data. It then outlines the data flow system including developing dimension tables for date, time of day, visitor, object, referrer, status, browser, and operating system. Finally, it specifies reports on statistics of visits, most/least popular pages, visitor locations, frequent visitors, top referrers/keywords, and most used browsers and operating systems.
Web mining
This document discusses web mining and its various types and applications. It defines web mining as the extraction of useful information from web documents and services. There are three main types of web mining: web content mining analyzes the content of web pages, web structure mining examines the link structure between pages, and web usage mining studies user access patterns by analyzing server logs. The challenges and pros of web mining are also covered, along with its applications in areas like e-commerce, business intelligence, and knowledge management.
Web mining
General Idea about web mining and different methods of web mining and terminologies associated with web mining and Usage of web mining, differentiation between web mining and data mining.
Web mining (1)
This document discusses web mining and its various types. Web mining involves using data mining techniques to discover useful information from web documents and usage patterns. It can involve content mining of text, images, video and audio to extract useful information. It also includes structure mining, which analyzes the hyperlink structure between documents and within documents. Additionally, web usage mining analyzes log files from web servers and applications to discover interesting usage patterns. The document outlines the differences between traditional data mining and web mining. It provides examples of applications of web mining such as information retrieval, network management and e-commerce.
Web mining
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.
Web mining
This document outlines a presentation on web mining. It begins with an introduction comparing data mining and web mining, noting that web mining extracts information from the world wide web. It then discusses the reasons for and types of web mining, including web content, structure, and usage mining. The document also covers the architecture and applications of web mining, challenges, and provides recommendations.
Web mining
This document discusses different types of web mining including web usage mining, web content mining, and web structure mining. It provides examples of web usage mining including tracking user browsing behavior and usage patterns to target customers and enhance experiences. Web structure mining aims to discover the link structure of websites and identify related pages. It describes techniques like PageRank and HITS algorithm. The document also provides a practical example of analyzing usage data according to demographics and a challenges section discussing issues around web scale and diversity.
Web Usage Pattern
This document discusses web usage mining. It begins by defining web mining and its three categories: web content mining, web structure mining, and web usage mining. The main focus is on web usage mining, which involves discovering user navigation patterns and predicting user behavior. The key processes of web usage mining are preprocessing raw data, pattern discovery using algorithms, and pattern analysis. Pattern discovery techniques discussed include statistical analysis, clustering, classification, association rules, and sequential patterns. Potential applications are personalized recommendations, system improvements, and business intelligence. The document concludes by discussing future research directions such as usage mining on the semantic web and analyzing discovered patterns.
Web Mining
This document provides an overview of web mining and summarizes key concepts. It begins with definitions of data mining and web mining. The document then discusses three categories of web mining: web content mining, web usage mining, and web structure mining. Various matrix expressions used to represent web data are also introduced, including document-keyword co-occurrence matrices, adjacent matrices, and usage matrices. Finally, two common similarity functions - Pearson correlation coefficient and cosine similarity - are outlined.
Web mining tools
Web mining tools based on content mining,usage mining and structure mining. Tools : Tableau,R, Octoparse , Scrapy, Hits and Pagerank algo. also included.
Web mining
Web mining is the application of data mining techniques to discover patterns from the World Wide Web. As the name proposes, this is information gathered by mining the web
Web Mining
This document provides an overview of web mining. It defines web mining as using data mining techniques to automatically discover and extract information from web documents and services. It discusses the differences between web mining and data mining, and covers the main topics in web mining including web graph analysis, structured data extraction, and web advertising. It also describes the different approaches of web content mining, web structure mining, and web usage mining.
Web Mining Presentation Final
The document presents an overview of web mining, which is defined as the application of data mining techniques to extract knowledge from web data, including web content, structure, and usage data. It discusses approaches to web content mining such as using databases or intelligent agents. Specific problems addressed in web content mining are also outlined, such as data extraction, schema matching, and opinion extraction. The document then describes approaches to web content mining including using multilevel databases, web query systems, and information filtering/categorization agents.
Web content mining
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.
Web mining
Presentation on Web Mining Or Data Mining. Data mining is the process of discovering insightful, interesting, and novel patterns, as well as descriptive, understandable and predictive models from large-scale data. data analysis aims to explore the numeric and categorical attributes of the data individually or jointly to extract key characteristics of the data sample via statistics that give information about the centrality, dispersion, and so on.
Web mining slides
This document discusses web mining and outlines its goals, types, and techniques. Web mining involves examining data from the world wide web and includes web content mining, web structure mining, and web usage mining. Content mining analyzes web page contents, structure mining analyzes hyperlink structures, and usage mining analyzes web server logs and user browsing patterns. Common techniques discussed include page ranking algorithms, focused crawlers, usage pattern discovery, and preprocessing of web server logs.
Web usage mining
This document provides an overview of web usage mining. It discusses that web usage mining applies data mining techniques to discover usage patterns from web data. The data can be collected at the server, client, or proxy level. The goals are to analyze user behavioral patterns and profiles, and understand how to better serve web applications. The process involves preprocessing data, pattern discovery using methods like statistical analysis and clustering, and pattern analysis including filtering patterns. Web usage mining can benefit applications like personalized marketing and increasing profitability.
Web mining
The World Wide Web (Web) is a popular and interactive medium to disseminate information today.
The Web is huge, diverse, and dynamic and thus raises the scalability, multi-media data, and temporal issues respectively.
Webmining ppt
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.
webmining overview
This document provides an introduction to web mining. It discusses what web mining is, how it differs from traditional data mining, and some of the key topics in web mining, including web graph analysis, power laws, structured data extraction from the web, web advertising, and systems issues related to mining very large web datasets.
Web mining
Web mining applies data mining techniques to web documents and services to extract knowledge. It aims to make the web more useful and profitable by increasing efficiency of interaction. Web mining includes web usage mining, web structure mining, and web content mining to discover useful information from web contents, links, and usage data. Analysis of web server logs can reveal patterns like popular pages and how users navigate a site. This information can then be used to improve site performance and design, detect intrusions, predict user behavior, and enhance personalization.
Webmining Overview
Web mining involves analyzing textual and link structure data from the world wide web to discover useful information. It deals with petabytes of data generated daily and needs to adapt to evolving usage patterns in real-time. Topics related to web mining include web graph analysis, power laws, structured data extraction, web advertising, user analysis, social networks, and blog analysis. The future will involve very large-scale data mining of datasets too big to fit in memory or even on a single disk.
WEB MINING.
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.
Gaurav web mining
Hey, this presentation would let you cover up with the concept of Web Mining. This was the presentation that i presented as my class assignment. This ppt. covers up the headlines of the topic "Web Mining" and lists the characteristics for the same. hope you guys find it useful. Thanks in Advance.
A survey on web usage mining techniques
This document summarizes a survey on web usage mining techniques presented by Mr. Abdul Rahaman Wahab Sait from Shaqra University in Saudi Arabia and Dr. Meyappan from Alagappa University in India. It introduces web usage mining and its role in determining customer behavior patterns from web data to help e-businesses. It then describes common web usage mining techniques like association rules, clustering, and classification. The document reviews past research applying clustering and classification algorithms to web usage mining and concludes that these techniques can help create intelligent websites, but further study is still needed to develop new automated techniques.
Web mining
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.
Personal Web Usage Mining
This document discusses personal web usage mining, which involves analyzing individual user's web browsing and navigation data recorded on the client side, rather than server side web logs. It proposes recording both remote activities sent to web servers as well as local on-desktop activities in an activity log. This log, along with cached web pages, would be stored and processed in a data warehouse to facilitate data mining and the development of tools and applications to understand users' interests and enhance their web experience.
Discovering knowledge using web structure mining
This document discusses web mining and algorithms for analyzing link structure on the web. It defines web mining as the process of discovering useful information from web data. There are three categories of web mining: web content mining, web structure mining, and web usage mining. Two important algorithms for analyzing hyperlink structure are HITS and PageRank. HITS identifies authoritative and hub pages, while PageRank calculates the importance of pages based on the number and quality of inbound links. The document provides details on how these algorithms work and potential applications.
Web mining application &trends in data mining
This document discusses web mining applications and trends in data mining. It defines web mining as using data mining techniques to automatically discover and extract information from web documents and services. The main applications of web mining are improving web search engines, predicting user behavior, and optimizing websites. Web mining can be divided into web content mining, web structure mining, and web usage mining. The document also outlines trends in data mining such as exploring new applications, developing scalable and interactive methods, and mining complex data types like biological data.
Web Mining
Here is a Presentation regarding web mining which is a blooming technology in the industry,here i have covered all the topics required for presentation. Hope u enjoy it.Please encourage to post more presentation documents.I can provide u the document also ,if anyone need comment below.
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Web Mining
This document provides an overview of web mining and summarizes key concepts. It begins with definitions of data mining and web mining. The document then discusses three categories of web mining: web content mining, web usage mining, and web structure mining. Various matrix expressions used to represent web data are also introduced, including document-keyword co-occurrence matrices, adjacent matrices, and usage matrices. Finally, two common similarity functions - Pearson correlation coefficient and cosine similarity - are outlined.
Web mining tools
Web mining tools based on content mining,usage mining and structure mining. Tools : Tableau,R, Octoparse , Scrapy, Hits and Pagerank algo. also included.
Web mining
Web mining is the application of data mining techniques to discover patterns from the World Wide Web. As the name proposes, this is information gathered by mining the web
Web Mining
This document provides an overview of web mining. It defines web mining as using data mining techniques to automatically discover and extract information from web documents and services. It discusses the differences between web mining and data mining, and covers the main topics in web mining including web graph analysis, structured data extraction, and web advertising. It also describes the different approaches of web content mining, web structure mining, and web usage mining.
Web Mining Presentation Final
The document presents an overview of web mining, which is defined as the application of data mining techniques to extract knowledge from web data, including web content, structure, and usage data. It discusses approaches to web content mining such as using databases or intelligent agents. Specific problems addressed in web content mining are also outlined, such as data extraction, schema matching, and opinion extraction. The document then describes approaches to web content mining including using multilevel databases, web query systems, and information filtering/categorization agents.
Web content mining
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.
Web mining
Presentation on Web Mining Or Data Mining. Data mining is the process of discovering insightful, interesting, and novel patterns, as well as descriptive, understandable and predictive models from large-scale data. data analysis aims to explore the numeric and categorical attributes of the data individually or jointly to extract key characteristics of the data sample via statistics that give information about the centrality, dispersion, and so on.
Web mining slides
This document discusses web mining and outlines its goals, types, and techniques. Web mining involves examining data from the world wide web and includes web content mining, web structure mining, and web usage mining. Content mining analyzes web page contents, structure mining analyzes hyperlink structures, and usage mining analyzes web server logs and user browsing patterns. Common techniques discussed include page ranking algorithms, focused crawlers, usage pattern discovery, and preprocessing of web server logs.
Web usage mining
This document provides an overview of web usage mining. It discusses that web usage mining applies data mining techniques to discover usage patterns from web data. The data can be collected at the server, client, or proxy level. The goals are to analyze user behavioral patterns and profiles, and understand how to better serve web applications. The process involves preprocessing data, pattern discovery using methods like statistical analysis and clustering, and pattern analysis including filtering patterns. Web usage mining can benefit applications like personalized marketing and increasing profitability.
Web mining
The World Wide Web (Web) is a popular and interactive medium to disseminate information today.
The Web is huge, diverse, and dynamic and thus raises the scalability, multi-media data, and temporal issues respectively.
Webmining ppt
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.
webmining overview
This document provides an introduction to web mining. It discusses what web mining is, how it differs from traditional data mining, and some of the key topics in web mining, including web graph analysis, power laws, structured data extraction from the web, web advertising, and systems issues related to mining very large web datasets.
Web mining
Web mining applies data mining techniques to web documents and services to extract knowledge. It aims to make the web more useful and profitable by increasing efficiency of interaction. Web mining includes web usage mining, web structure mining, and web content mining to discover useful information from web contents, links, and usage data. Analysis of web server logs can reveal patterns like popular pages and how users navigate a site. This information can then be used to improve site performance and design, detect intrusions, predict user behavior, and enhance personalization.
Webmining Overview
Web mining involves analyzing textual and link structure data from the world wide web to discover useful information. It deals with petabytes of data generated daily and needs to adapt to evolving usage patterns in real-time. Topics related to web mining include web graph analysis, power laws, structured data extraction, web advertising, user analysis, social networks, and blog analysis. The future will involve very large-scale data mining of datasets too big to fit in memory or even on a single disk.
WEB MINING.
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.
Gaurav web mining
Hey, this presentation would let you cover up with the concept of Web Mining. This was the presentation that i presented as my class assignment. This ppt. covers up the headlines of the topic "Web Mining" and lists the characteristics for the same. hope you guys find it useful. Thanks in Advance.
A survey on web usage mining techniques
This document summarizes a survey on web usage mining techniques presented by Mr. Abdul Rahaman Wahab Sait from Shaqra University in Saudi Arabia and Dr. Meyappan from Alagappa University in India. It introduces web usage mining and its role in determining customer behavior patterns from web data to help e-businesses. It then describes common web usage mining techniques like association rules, clustering, and classification. The document reviews past research applying clustering and classification algorithms to web usage mining and concludes that these techniques can help create intelligent websites, but further study is still needed to develop new automated techniques.
Web mining
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.
Personal Web Usage Mining
This document discusses personal web usage mining, which involves analyzing individual user's web browsing and navigation data recorded on the client side, rather than server side web logs. It proposes recording both remote activities sent to web servers as well as local on-desktop activities in an activity log. This log, along with cached web pages, would be stored and processed in a data warehouse to facilitate data mining and the development of tools and applications to understand users' interests and enhance their web experience.
Discovering knowledge using web structure mining
This document discusses web mining and algorithms for analyzing link structure on the web. It defines web mining as the process of discovering useful information from web data. There are three categories of web mining: web content mining, web structure mining, and web usage mining. Two important algorithms for analyzing hyperlink structure are HITS and PageRank. HITS identifies authoritative and hub pages, while PageRank calculates the importance of pages based on the number and quality of inbound links. The document provides details on how these algorithms work and potential applications.
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This document discusses web mining applications and trends in data mining. It defines web mining as using data mining techniques to automatically discover and extract information from web documents and services. The main applications of web mining are improving web search engines, predicting user behavior, and optimizing websites. Web mining can be divided into web content mining, web structure mining, and web usage mining. The document also outlines trends in data mining such as exploring new applications, developing scalable and interactive methods, and mining complex data types like biological data.
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Here is a Presentation regarding web mining which is a blooming technology in the industry,here i have covered all the topics required for presentation. Hope u enjoy it.Please encourage to post more presentation documents.I can provide u the document also ,if anyone need comment below.
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This document discusses different types of web mining techniques. It begins by defining web mining as the application of data mining techniques to discover and extract information from web data. The three main types of web mining are discussed as web content mining, web structure mining, and web usage mining. Web content mining involves mining the actual contents within web pages and documents. Web structure mining mines the hyperlink structure of websites to determine how web pages are linked together. Web usage mining mines web server logs to discover user browsing patterns and behaviors.
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This document discusses research issues in web mining. It provides an overview of the three categories of web mining: web content mining, web structure mining, and web usage mining.
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Web usage mining analyzes user behavior by mining web server logs. It involves preprocessing log data, discovering patterns using techniques like clustering and rules, and analyzing patterns. Challenges include session identification and handling dynamic pages. Overall, the document outlines the key techniques and ongoing research problems in the different areas
RESEARCH ISSUES IN WEB MINING
Web is a collection of inter-related files on one or more web servers while web mining means extracting
valuable information from web databases. Web mining is one of the data mining domains where data
mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer
behaviour, evaluate a particular website based on the information which is stored in web log files. Web
mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library.
Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase
the complexity of dealing information from different web service providers. The collection of information
becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
RESEARCH ISSUES IN WEB MINING
Web is a collection of inter-related files on one or more web servers while web mining means extracting
valuable information from web databases. Web mining is one of the data mining domains where data
mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer
behaviour, evaluate a particular website based on the information which is stored in web log files. Web
mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library.
Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase
the complexity of dealing information from different web service providers. The collection of information
becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
RESEARCH ISSUES IN WEB MINING
Web is a collection of inter-related files on one or more web servers while web mining means extracting valuable information from web databases. Web mining is one of the data mining domains where data mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer behaviour, evaluate a particular website based on the information which is stored in web log files. Web mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library. Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase the complexity of dealing information from different web service providers. The collection of information becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
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Web mining
- 1. WEB MINING
- 2. What is Web Mining? Discovering useful information from the World-Wide Web and its usage patterns
- 3. Web Mining v/s Data Mining Data Mining: turn data into knowledge. Web mining: It is an application of data mining technique to find interesting & potentially useful knowledge from web data.
- 5. Web Mining Challenges Data is huge Data is diverse Data is redundant
- 6. Types of Web Mining
- 7. Web Content Mining Web Content Mining is the process of extracting useful information from the content of web documents. It may consist of text , images, audio , video etc.
- 8. Web Structure Mining is the process of discovering structure information from the web. It categorize the web pages and generate similarity & relationship between different websites. Web Structure Mining
- 9. Web usage mining is the process of extracting useful information from server logs i.e. user history. It is process of finding out what users are looking on internet. Web Usage Mining
- 10. Application of Web Mining Present dynamic information to users based on their interest Improve site design Predict customer’s behaviour Web Wide Tracking
- 11. Thank You!
Editor's Notes
- Google’s usage logs are much bigger than their web crawl! Order of magnitude: terabytes per day No human in the loop