Independent component analysis and rough fuzzy based approach to web usage mining

Authors:
Siriporn Chimphlee;Naomie Salim;Mohd Salihin Bin Ngadiman;Witcha Chimphlee;Surat Srinoy
Affiliations:
Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, Thailand;Faculty of Computer Science and Information Systems, University Technology of Malaysia, Skudai, Johor, Malaysia;Faculty of Computer Science and Information Systems, University Technology of Malaysia, Skudai, Johor, Malaysia;Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, Thailand;Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, Thailand
Venue:
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Year:
2006

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Abstract

Web Usage Mining is that area of Web Mining which deals with the extraction of interesting knowledge from logging information produced by Web servers. A challenge in web classification is how to deal with the high dimensionality of the feature space. In this paper we present Independent Component Analysis (ICA) for feature selection and using Rough Fuzzy for clustering web user sessions. It aims at discovery of trends and regularities in web users' access patterns. ICA is a very general-purpose statistical technique in which observed random data are linearly transformed into components that are maximally independent from each other, and simultaneously have "interesting" distributions. Our experiments indicate can improve the predictive performance when the original feature set for representing web log is large and can handling the different groups of uncertainties/impreciseness accuracy.