Mining usage web log via independent component analysis and rough fuzzy

Authors:
Siriporn Chimphlee;Naomie Salim;Mohd Salihin Bin Ngadiman;Witcha Chimphlee;Surat Srinoy
Affiliations:
Faculty of Science and Technology, Suan Dusit Rajabhat University, Bangkok, Thailand;Faculty of Computer Science and Information Systems, University Technology of Malaysia, Skudai, Johor, Malayasia;Faculty of Computer Science and Information Systems, University Technology of Malaysia, Skudai, Johor, Malayasia;Faculty of Science and Technology, Suan Dusit Rajabhat University, Bangkok, Thailand;Suan Dusit Rajabhat University, Bangkok, Thailand
Venue:
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Year:
2006

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Abstract

In the past few years, web usage mining techniques have grown rapidly together with the explosive growth of the web, both in the research and commercial areas. 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. 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.