JACIII Vol.22 No.4 pp. 537-543
doi: 10.20965/jaciii.2018.p0537


Fuzzified Even-Sized Clustering Based on Optimization

Kei Kitajima*, Yasunori Endo**, and Yukihiro Hamasuna***

*Department of Risk Engineering, 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

***Department of Informatics, School of Science and Engineering, Kindai University
3-4-1 Kowakae, Higashiosaka, Osaka 577-8502, Japan

December 20, 2017
April 12, 2018
July 20, 2018

Clustering is a method of data analysis without the use of supervised data. Even-sized clustering based on optimization (ECBO) is a clustering algorithm that focuses on cluster size with the constraints that cluster sizes must be the same. However, this constraints makes ECBO inconvenient to apply in cases where a certain margin of cluster size is allowed. It is believed that this issue can be overcome by applying a fuzzy clustering method. Fuzzy clustering can represent the membership of data to clusters more flexible. In this paper, we propose a new even-sized clustering algorithm based on fuzzy clustering and verify its effectiveness through numerical examples.

Cite this article as:
K. Kitajima, Y. Endo, and Y. Hamasuna, “Fuzzified Even-Sized Clustering Based on Optimization,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.4, pp. 537-543, 2018.
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