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Independent Component Analysis And Rough Fuzzy Based Approach To Web Usage Mining

Chimphlee, Siriporn and Salim, Naomie and Ngadiman, Mohd Salihin and Chimphlee , Witcha and Srinoy, Surat (2006) Independent Component Analysis And Rough Fuzzy Based Approach To Web Usage Mining. In: A Publicational of the International Association of Science and Technology for Develpoment, 13-16 February 2006, Innsbruck Aurtria.

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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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Web Usage Mining; Independent component analysis;Rough Sets; Fuzzy rough sets;
Subjects:Q Science > QA Mathematics > QA76 Computer software
Divisions:Computer Science and Information System (Formerly known)
ID Code:3180
Deposited By: Norazlizafarah Anuar
Deposited On:24 May 2007 07:27
Last Modified:01 Jun 2010 03:07

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