Gaurav web mining

Gaurav web mining

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  WEB MINING Presented by: GauravUniyal 161340101008 C.S.E.(Final Year)
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  Introduction • Web miningis 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.
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  Web Mining Services Thistechnology has enabled e-commerce to do personalized marketing, which eventually results in higher trade volumes. Which eventually results in higher trade volumes.
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  WWW Describes…  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.
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  Tasks to Conduct •Resource Finding. • Information selection & Pre-processing. • Generalization. • Analysis.
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  Web Mining Classification WebMining Web Content Mining Web Structure Mining Web Usage Mining Search Result Mining Customized Usage Tracking Web Page Content Mining General Access Pattern Tracking
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  Web Content Mining Discovery of useful information from web contents /data /documents.  Information Retrieval view.  Database View.
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  Web Structure Mining 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)
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  Web Usage Mining 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
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   Generate simplestatistical 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 report.
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   Learn: • Whois visiting you site. • The path visitors take through your pages. • How much time visitors spend on each page. • The most common starting page. • What content are your visitors going through. • Where visitors are leaving your site.
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  Design of WebLog Miner  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
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  Structures  Hubs.  Authority. Mutual Reinforcing Relationship.  Hyperlinks can infer  The notation of Authority.
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  Structures
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  HITS  HITS Standsfor 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. e.g. 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.
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  Application of WebMining  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.
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  Thank You!