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Data clustering using variable precision rough set

Yanto, Iwan Tri Riyadi and Herawan, Tutut and Mat Deris, Mustafa (2011) Data clustering using variable precision rough set. Intelligent Data Analysis, 15 (4). pp. 465-482. ISSN 1088-467X

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Clustering a set of objects into homogeneous classes is a fundamental operation in data mining. Several cluster analysis techniques have been developed to group objects having similar characteristics. Recently, many attentions have been put on categorical data clustering, where data objects are made up of non-numerical attributes. An algorithm termed MMR using classical rough set theory was proposed to deal with problems in clustering categorical data. However, the MMR algorithm fails to handle noisy data as an integral part of databases. In this paper, an alternative technique for clustering noisy categorical data using Variable Precision Rough Set model is proposed. The results show that the technique provides better performance in selecting the clustering attribute.

Item Type: Article
Uncontrolled Keywords: clustering; rough set; variable precision rough set model
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Normajihan Abd. Rahman
Date Deposited: 15 Apr 2013 05:18
Last Modified: 15 Apr 2013 05:18
URI: http://eprints.uthm.edu.my/id/eprint/3582
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