Research Article Open Access

Classification and Clustering of Web Log Data to Analyze User Navigation Patterns

Abstract

The information explosion in World Wide Web has increased the interest in Web usage mining techniques in both commercial and academic areas. Study of interested web users; provide valuable information for web designers to quickly respond to their individual needs and for the efficient organization of the website. Among the several approaches, like, Association rule mining, classification, clustering, to extract knowledge from user’s navigation data, this paper uses clustering and classification of log data to discover knowledge from web log files. The proposed algorithm uses Expectation Maximization (EM) clustering along with Maximum Likelihood classification for knowledge discovery from user’s navigation patterns. Experiments have been carried out in order to validate the proposed approach and evaluate the proposed algorithm.

Mrs.Niranjana.Kannan and Dr (Mrs).Elizabeth Shanthi

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