JACIII Vol.19 No.1 pp. 29-35
doi: 10.20965/jaciii.2015.p0029


On Objective-Based Rough Hard and Fuzzy c-Means Clustering

Naohiko Kinoshita* and Yasunori Endo**

*Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
**Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

April 20, 2014
August 25, 2014
Online released:
January 20, 2015
January 20, 2015
clustering, rough clustering, rough set theory, optimization

Clustering is one of the most popular unsupervised classification methods. In this paper, we focus on rough clustering methods based on rough-set representation. Rough k-Means (RKM) is one of the rough clustering method proposed by Lingras et al. Outputs of many clustering algorithms, including RKM depend strongly on initial values, so we must evaluate the validity of outputs. In the case of objectivebased clustering algorithms, the objective function is handled as the measure. It is difficult, however to evaluate the output in RKM, which is not objective-based. To solve this problem, we propose new objective-based rough clustering algorithms and verify theirs usefulness through numerical examples.

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Last updated on Mar. 24, 2017