Data Mining and Knowledge Engineering

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Improvements in Dynamic Web Sites and Analysing User Profile Using Web Mining & Clustering


Affiliations
1 Department of Computer Science and Engineering, Raja College of Engineering and Technology, Madurai, Tamilnadu, India
2 Department of Information Technology, Velammal College of Engineering and Technology, Madurai, Tamilnadu, India
3 Department of Electronics and Communication Engineering, Velammal College of Engineering and Technology, Madurai, Tamilnadu, India
     

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A complete framework and findings are presented in mining web usage patterns from web log files of a real web site that has all the challenging aspects of real-life web usage mining, including evolving user profiles and external data describing anontology of the web content. The behavior of a web site's users may change so quickly that attempting to make predictions, according to the frequent patterns coming from the analysis of an access log file, becomes challenging. In order for the obsolescence of the behavioral patterns to become as possible, the ideal method would provide frequent patterns in real time, allowing the result to be available immediately. Even though the web site under study is part of a nonprofit organization that does not "sell" any products, it was crucial to understand "who" the users Ire, "what" they looked at, and "how their interests changed with time," all of which are important questions in Customer Relationship Management (CRM). Hence, this is a present approach for discovering and tracking evolving user profiles here also describes how the discovered user profiles can be enriched with explicit information need that is inferred from search queries extracted from web log data. An objective validation strategy is also used to assess the quality of the mined profiles, in particular their adaptability in the face of evolving user behavior. In this paper a method allowing to find frequent behavioral patterns in real time, whatever the number of connected users is.
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  • Improvements in Dynamic Web Sites and Analysing User Profile Using Web Mining & Clustering

Abstract Views: 181  |  PDF Views: 3

Authors

P. Senthil Pandian
Department of Computer Science and Engineering, Raja College of Engineering and Technology, Madurai, Tamilnadu, India
P. Senthil Kumar
Department of Information Technology, Velammal College of Engineering and Technology, Madurai, Tamilnadu, India
N. Suresh Kumar
Department of Electronics and Communication Engineering, Velammal College of Engineering and Technology, Madurai, Tamilnadu, India

Abstract


A complete framework and findings are presented in mining web usage patterns from web log files of a real web site that has all the challenging aspects of real-life web usage mining, including evolving user profiles and external data describing anontology of the web content. The behavior of a web site's users may change so quickly that attempting to make predictions, according to the frequent patterns coming from the analysis of an access log file, becomes challenging. In order for the obsolescence of the behavioral patterns to become as possible, the ideal method would provide frequent patterns in real time, allowing the result to be available immediately. Even though the web site under study is part of a nonprofit organization that does not "sell" any products, it was crucial to understand "who" the users Ire, "what" they looked at, and "how their interests changed with time," all of which are important questions in Customer Relationship Management (CRM). Hence, this is a present approach for discovering and tracking evolving user profiles here also describes how the discovered user profiles can be enriched with explicit information need that is inferred from search queries extracted from web log data. An objective validation strategy is also used to assess the quality of the mined profiles, in particular their adaptability in the face of evolving user behavior. In this paper a method allowing to find frequent behavioral patterns in real time, whatever the number of connected users is.