A rough set approach for selecting clustering attribute

Authors:
Tutut Herawan;Mustafa Mat Deris;Jemal H. Abawajy
Affiliations:
University of Tun Hussein Onn Malaysia, Faculty of Information Technology and Multimedia, Parit Raja, 86400, Batu Pahat, Johor, Malaysia;University of Tun Hussein Onn Malaysia, Faculty of Information Technology and Multimedia, Parit Raja, 86400, Batu Pahat, Johor, Malaysia;Deakin University, School of Engineering and Information Technology, Geelong, VIC, Australia
Venue:
Knowledge-Based Systems
Year:
2010

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Abstract

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.