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Web mining, importance and techniques

Short answer

Web mining applies data mining to discover patterns from web content, structure, and usage logs. It is important because the web is huge and dynamic, and mining improves search, personalization, e-commerce analytics, and fraud detection. Techniques are content mining, structure mining (PageRank, HITS), and usage mining from server logs.

What is web mining? Explain the importance of World Wide Web Mining. Categorize and briefly describe the WWW mining techniques.

Web mining is the application of data mining techniques to automatically discover and extract useful information and patterns from web documents, services and logs.

Importance of WWW mining

  • The Web is a huge, growing, dynamic and heterogeneous source of information, so users cannot find information by manual search.
  • It supports better search and ranking, and personalisation (recommendations).
  • Businesses use it for e-commerce analytics: customer behaviour, targeted advertising, site improvement, and CRM.
  • Detecting fraud, spam and security threats, and understanding social networks and opinions.
  • Helps in web site design, caching and reducing network traffic.

Categories (techniques) of web mining

            Web mining
   ________/    |      \________
Content      Structure      Usage
 mining       mining        mining
  1. Web content mining: extracts useful knowledge from the content of pages (text, images, audio, video, tables). Techniques: text mining, classification and clustering of pages, information extraction, summarisation, opinion mining; wrapper induction. Used for search engines and recommendations.
  2. Web structure mining: analyses the hyperlink structure (graph of pages) to find the importance of pages and communities. Techniques: PageRank, HITS (hubs and authorities), link analysis, community detection. Used for ranking and crawling.
  3. Web usage mining: discovers patterns in the user's access from web server logs, cookies and click-streams. Steps: preprocessing (data cleaning, user and session identification), pattern discovery (association rules, sequential patterns, clustering), pattern analysis. Used for personalisation, site improvement and recommendation.

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