JACIII Vol.22 No.6 pp. 956-964
doi: 10.20965/jaciii.2018.p0956


Rough Set-Based Clustering Utilizing Probabilistic Memberships

Seiki Ubukata, Hiroki Kato, Akira Notsu, and Katsuhiro Honda

Osaka Prefecture University
1-1 Gakuen-cho, Nakaku, Sakai, Osaka 599-8531, Japan

February 17, 2018
July 24, 2018
October 20, 2018
clustering, rough set theory, rough C-means, rough set C-means, rough membership C-means
Rough Set-Based Clustering Utilizing Probabilistic Memberships

Comparison of the classification performance of rough set-based clustering utilizing probabilistic membership, i.e., π generalized rough C-means (πGRCM), π rough set C-means (πRSCM), and rough membership C-means (RMCM), in Breast Cancer Wisconsin dataset. Our method (RMCM) achieved the best performance.

Representing the positive, possible, and boundary regions of clusters, rough set-based C-means clustering methods, such as generalized rough C-means (GRCM) and rough set C-means (RSCM), are promising for analyzing vague cluster shapes and realizing reliable classification. In this study, we consider rough set-based clustering approaches that utilize probabilistic memberships as variants of GRCM and RSCM, including π generalized rough C-means (πGRCM), π rough set C-means (πRSCM), and rough membership C-means (RMCM). πGRCM and πRSCM assign equal probabilities of cluster belonging according to Laplace’s principle of indifference, whereas RMCM assigns the probabilities according to rough memberships, which represent conditional probabilities based on the object’s neighborhood derived from a binary relation. In addition, we discuss the theoretical validity of our RMCM approach and compare it with other methods considered in this study. Furthermore, we conducted numerical experiments for evaluating the classification performances of the abovementioned methods. Based on our experimental results, the methods were found to be effective.

Cite this article as:
S. Ubukata, H. Kato, A. Notsu, and K. Honda, “Rough Set-Based Clustering Utilizing Probabilistic Memberships,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.6, pp. 956-964, 2018.
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