Web mining

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Web mining

  1. 1. 10/9/2013 1
  2. 2. Web mining is to apply data mining techniques to extract and uncover knowledge from web documents and services. Using data mining techniques to make the web more useful and more profitable and to increase the efficiency of our interaction with the web. 10/9/2013 2
  3. 3. 10/9/2013 3
  4. 4. Web: A huge, widely-distributed, highly heterogeneous, semi-structured, hypertext/hypermedia, interconnected information repository. Web is a huge collection of documents plus – Hyper-link information – Access and usage information 10/9/2013 4
  5. 5. Resource Finding. Information selection & Pre-processing. Generalization. Analysis. 10/9/2013 5
  6. 6. WEB MINING WEB USAGE MINING WEB STRUCTURE MINING WEB CONTENT MINING CUSTOMIZED USAGE TRACKING GENERAL ACCESS PATTERN TRACKING SEARCH RESULT MINING WEB PAGE CONTENT MINING 10/9/2013 6
  7. 7. Discovery of useful information from web contents /data /documents. Information Retrieval view. Database View. 10/9/2013 7
  8. 8. Researchers proposed methods of using citations among journal articles to evaluate the quality of research papers. Customer behavior – evaluate a quality of a product based on the opinions of other customers (instead of product’s description or advertisement). 10/9/2013 8
  9. 9. It’s also known as Web log Mining. DEFINITION Discovery of meaningful patterns from data generated by client-server transactions (or) from Web server logs. Typical Sources of Data: automatically generated data stored in server access logs, referrer logs, agent logs, and client-side cookies. user profiles. metadata: page attributes, content attributes, usage data. 10/9/2013 9
  10. 10. Generate simple statistical reports: A summary report of hits and bytes transferred A list of top requested URLs A list of top referrers A list of most common browsers used Hits per hour/day/week/month reports Hits per domain reports Learn: Who is visiting you site The path visitors take through your pages How much time visitors spend on each page The most common starting page Where visitors are leaving your site 10/9/2013 10
  11. 11. Weblog is Filtered to generate a relational Database. A Data cube is generated from Database. OLAP is used to drill-down and roll-up in the cube. 10/9/2013 11 WEB LOG Database Data Cleaning Knowledge Patterns Data cube creation Data cube Sliced and diced cube Data Mining OLAP
  12. 12. Hubs. Authority. Mutual Reinforcing Relationship. Finding Authoritative Web Pages. Hyperlinks can infer the notation of Authority. 10/9/2013 12 HUBS AUTHORITIES Hub-Authority Relations
  13. 13. 10/9/2013 13
  14. 14. HITS Stands for Hyperlink-Induced Topic Search. It Explore interactions between hubs and authoritative pages. Expand the root set into a base set. Apply Weight-Propagation. System Based on the HITS Algorithm. - eg) GOOGLE. Difficulties from ignoring textual contexts -Drifting: When Hubs contains Multiple Topics. -Topic hijacking: When Many Pages from a single web site point to the same single Popular site. 10/9/2013 14
  15. 15. Improve web server system performance. Improve site Design. Intrusion Detection. Predict user’s Action. Enhance the quality and delivery of the internet information services to the end user. Facilitates Adaptive sites/personalization. 10/9/2013 15
  16. 16. 10/9/2013 16

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