Mining Usage Web Log Via Independent Component Analysis And Rough Fuzzy
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 We...
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my.utm.31812017-08-24T03:57:08Z http://eprints.utm.my/id/eprint/3181/ Mining Usage Web Log Via Independent Component Analysis And Rough Fuzzy Siriporn, Chimphlee Salim, Naomie Ngadiman, Mohd. Salihin Witcha, Chimphlee Surat, Srinoy QA76 Computer software 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. 2006-02 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/3181/1/B-06_DrSalihin-Madrid.pdf Siriporn, Chimphlee and Salim, Naomie and Ngadiman, Mohd. Salihin and Witcha, Chimphlee and Surat, Srinoy (2006) Mining Usage Web Log Via Independent Component Analysis And Rough Fuzzy. In: procedings of the 5th WSEAS International Conference on, 15-17 february 2006, Masrid, Spain.. |
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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. |
format |
Conference or Workshop Item |
author |
Siriporn, Chimphlee Salim, Naomie Ngadiman, Mohd. Salihin Witcha, Chimphlee Surat, Srinoy |
author_facet |
Siriporn, Chimphlee Salim, Naomie Ngadiman, Mohd. Salihin Witcha, Chimphlee Surat, Srinoy |
author_sort |
Siriporn, Chimphlee |
title |
Mining Usage Web Log Via Independent Component Analysis And Rough Fuzzy
|
title_short |
Mining Usage Web Log Via Independent Component Analysis And Rough Fuzzy
|
title_full |
Mining Usage Web Log Via Independent Component Analysis And Rough Fuzzy
|
title_fullStr |
Mining Usage Web Log Via Independent Component Analysis And Rough Fuzzy
|
title_full_unstemmed |
Mining Usage Web Log Via Independent Component Analysis And Rough Fuzzy
|
title_sort |
mining usage web log via independent component analysis and rough fuzzy |
publishDate |
2006 |
url |
http://eprints.utm.my/id/eprint/3181/1/B-06_DrSalihin-Madrid.pdf http://eprints.utm.my/id/eprint/3181/ |
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