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

Paper:

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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

Received:
April 20, 2014
Accepted:
August 25, 2014
Published:
January 20, 2015
Creative Commons License
Keywords:
clustering, rough clustering, rough set theory, optimization
Abstract
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.
Cite this article as:
N. Kinoshita and Y. Endo, “On Objective-Based Rough Hard and Fuzzy c-Means Clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.1, pp. 29-35, 2015.
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