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JACIII Vol.22 No.1 pp. 54-61
doi: 10.20965/jaciii.2018.p0054
(2018)

Paper:

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Two-Stage Clustering Based on Cluster Validity Measures

Yukihiro Hamasuna*, Ryo Ozaki**, and Yasunori Endo***

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

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

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

Received:
November 25, 2016
Accepted:
October 7, 2017
Published:
January 20, 2018
Creative Commons License
Keywords:
two-stage clustering, cluster validity measures, kernel method, c-means clustering, agglomerative hierarchical clustering
Abstract

To handle a large-scale object, a two-stage clustering method has been previously proposed. The method generates a large number of clusters during the first stage and merges clusters during the second stage. In this paper, a novel two-stage clustering method is proposed by introducing cluster validity measures as the merging criterion during the second stage. The significant cluster validity measures used to evaluate cluster partitions and determine the suitable number of clusters act as the criteria for merging clusters. The performance of the proposed method based on six typical indices is compared with eight artificial datasets. These experiments show that a trace of the fuzzy covariance matrix Wtr and its kernelization KWtr are quite effective when applying the proposed method, and obtain better results than the other indices.

Cite this article as:
Y. Hamasuna, R. Ozaki, and Y. Endo, “Two-Stage Clustering Based on Cluster Validity Measures,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.1, pp. 54-61, 2018.
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