RMF: Rough Set Membership Function-based for Clustering Web Transactions
One of the most important techniques to improve information management on the web in order to obtain better understanding of user's behaviour is clustering web data. Currently, the rough approximation-based clustering technique has been used to group web transactions into clusters. It is based...
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Online Access: | http://umpir.ump.edu.my/id/eprint/7504/1/RMF-_Rough_Set_Membership_Function-based_for_Clustering_Web_Transactions.pdf http://umpir.ump.edu.my/id/eprint/7504/ http://dx.doi.org/10.14257/ijmue.2013.8.6.11 |
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my.ump.umpir.75042015-03-03T09:36:29Z http://umpir.ump.edu.my/id/eprint/7504/ RMF: Rough Set Membership Function-based for Clustering Web Transactions Herawan, Tutut Wan Maseri, Wan Mohd QA76 Computer software One of the most important techniques to improve information management on the web in order to obtain better understanding of user's behaviour is clustering web data. Currently, the rough approximation-based clustering technique has been used to group web transactions into clusters. It is based on the similarity of upper approximations of transactions to merge between two or more clusters. However, in reviewing the technique, it has a weakness in terms of processing time in obtaining web clusters. In this paper, an alternative technique for grouping web transactions using rough set theory, named RMF is proposed. It is based on the rough membership function of a transaction similarity class with respect to the other classes. The two UCI benchmarks datasets are opted in the experimental processes. The experimental results reveal that the proposed technique has an benefit of low time complexity as compared to the baseline technique up to 67 % SERSC 2013 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/7504/1/RMF-_Rough_Set_Membership_Function-based_for_Clustering_Web_Transactions.pdf Herawan, Tutut and Wan Maseri, Wan Mohd (2013) RMF: Rough Set Membership Function-based for Clustering Web Transactions. International Journal of Multimedia and Ubiquitous Engineering (IJMUE), 8 (6). pp. 105-118. ISSN 1975-0080 http://dx.doi.org/10.14257/ijmue.2013.8.6.11 DOI: 10.14257/ijmue.2013.8.6.11 |
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One of the most important techniques to improve information management on the web in order to obtain better understanding of user's behaviour is clustering web data. Currently, the rough approximation-based clustering technique has been used to group web transactions into clusters. It is based on the similarity of upper approximations of transactions to merge between two or more clusters. However, in reviewing the technique, it has a weakness in terms of processing time in obtaining web clusters. In this paper, an alternative technique for
grouping web transactions using rough set theory, named RMF is proposed. It is based on the rough membership function of a transaction similarity class with respect to the other classes. The two UCI benchmarks datasets are opted in the experimental processes. The experimental results reveal that the proposed technique has an benefit of low time complexity as compared to the baseline technique up to 67 % |
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Article |
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Herawan, Tutut Wan Maseri, Wan Mohd |
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Herawan, Tutut Wan Maseri, Wan Mohd |
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Herawan, Tutut |
title |
RMF: Rough Set Membership Function-based for Clustering Web Transactions |
title_short |
RMF: Rough Set Membership Function-based for Clustering Web Transactions |
title_full |
RMF: Rough Set Membership Function-based for Clustering Web Transactions |
title_fullStr |
RMF: Rough Set Membership Function-based for Clustering Web Transactions |
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RMF: Rough Set Membership Function-based for Clustering Web Transactions |
title_sort |
rmf: rough set membership function-based for clustering web transactions |
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SERSC |
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2013 |
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http://umpir.ump.edu.my/id/eprint/7504/1/RMF-_Rough_Set_Membership_Function-based_for_Clustering_Web_Transactions.pdf http://umpir.ump.edu.my/id/eprint/7504/ http://dx.doi.org/10.14257/ijmue.2013.8.6.11 |
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