JACIII Vol.22 No.1 pp. 62-69
doi: 10.20965/jaciii.2018.p0062


Even-Sized Clustering Based on Optimization and its Variants

Yasunori Endo*, Yukihiro Hamasuna**, Tsubasa Hirano***, and Naohiko Kinoshita***

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

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

***Department of Risk Engineering, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

January 10, 2017
October 7, 2017
January 20, 2018
even-sized clustering, k-member clustering, optimization, linear programming

A clustering method referred to as K-member clustering classifies a dataset into certain clusters, the size of which is more than a given constant K. Even-sized clustering, which classifies a dataset into even-sized clusters, is also considered along with K-member clustering. In our previous study, we proposed Even-sized Clustering Based on Optimization (ECBO) to output adequate results by formulating an even-sized clustering problem as linear programming. The simplex method is used to calculate the belongingness of each object to clusters in ECBO. In this study, ECBO is extended by introducing ideas that were introduced in K-means or fuzzy c-means to resolve problems of initial-value dependence, robustness against outliers, calculation costs, and nonlinear boundaries of clusters. We also reconsider the relation between the dataset size, the cluster number, and K in ECBO. Moreover, we verify the effectiveness of the variants of ECBO based on experimental results using synthetic datasets and a benchmark dataset.

Cite this article as:
Y. Endo, Y. Hamasuna, T. Hirano, and N. Kinoshita, “Even-Sized Clustering Based on Optimization and its Variants,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.1, pp. 62-69, 2018.
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