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

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dc.contributor.authorSiriporn Chimphlee
dc.contributor.authorNaomie Salim
dc.contributor.authorMohd Salim Bin Ngadiman
dc.contributor.authorWitcha Chimphlee
dc.contributor.authorSurat Srinoy
dc.contributor.correspondenceS. Chimphlee; Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, 295 Rajasrima Rd, Thailand; email: siripom.chi@dusit.ac.th
dc.date.accessioned2025-03-10T07:38:08Z
dc.date.available2025-03-10T07:38:08Z
dc.date.issued2006
dc.description.abstractWeb 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.
dc.identifier.citationProceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2006
dc.identifier.scopus2-s2.0-38049169598
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/5064
dc.languageEnglish
dc.rights.holderScopus
dc.subjectFuzzy rough sets
dc.subjectIndependent component analysis
dc.subjectRough sets
dc.subjectWeb usage mining
dc.titleIndependent component analysis and rough fuzzy based approach to web usage mining
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-38049169598&partnerID=40&md5=7ff084f60dd881f8e40812b40f75e709
oaire.citation.endPage427
oaire.citation.startPage422
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