A rough set approach for selecting clustering attribute

Herawan, Tutut, Deris, Mustafa Mat and Abawajy, Jemal H. 2010, A rough set approach for selecting clustering attribute, Knowledge-based systems, vol. 23, no. 3, pp. 220-231, doi: 10.1016/j.knosys.2009.12.003.

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Title A rough set approach for selecting clustering attribute
Author(s) Herawan, Tutut
Deris, Mustafa Mat
Abawajy, Jemal H.ORCID iD for Abawajy, Jemal H. orcid.org/0000-0001-8962-1222
Journal name Knowledge-based systems
Volume number 23
Issue number 3
Start page 220
End page 231
Total pages 11
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2010-04
ISSN 0950-7051
Keyword(s) clustering
dependency of attributes
rough set theory
Summary 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.
Language eng
DOI 10.1016/j.knosys.2009.12.003
Field of Research 080403 Data Structures
Socio Economic Objective 890299 Computer Software and Services not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
HERDC collection year 2010
Copyright notice ©2009, Elsevier B.V.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30026302

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Created: Tue, 30 Mar 2010, 22:58:09 EST by Jemal Abawajy

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