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A rough set approach for selecting clustering attribute

Herawan, Tutut and Mat Deris, Mustafa and Abawajy, Jemal (2010) A rough set approach for selecting clustering attribute. Knowledge-Based Systems, 23 (3). pp. 220-231. ISSN 0950-7051

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A few of clustering techniques for categorical data exist to group objects having similar characteristics. Some are able to handle uncertainty in the clustering process while others have stability issues. However, the performance of these techniques is an issue due to low accuracy and high computational complexity. This paper proposes a new technique called maximum dependency attributes (MDA) for selecting clustering attribute. The proposed approach is based on rough set theory by taking into account the dependency of attributes of the database. We analyze and compare the performance of MDA technique with the bi-clustering, total roughness (TR) and min-min roughness (MMR) techniques based on four test cases. The results establish the better performance of the proposed approach.

Item Type: Article
Uncontrolled Keywords: clustering; dependency of attributes; performance; rough set theory
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 05:42
Last Modified: 31 Oct 2019 02:40
URI: http://eprints.uthm.edu.my/id/eprint/3589
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