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JACIII Vol.22 No.4 pp. 544-550
doi: 10.20965/jaciii.2018.p0544
(2018)

Paper:

Cluster Validity Measures for Network Data

Yukihiro Hamasuna*1, Daiki Kobayashi*2, Ryo Ozaki*3, and Yasunori Endo*4

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

*2Graduate School of Science and Engineering, Kindai University
3-4-1 Kowakae, Higashiosaka, Osaka 577-8502, Japan

*3ALBERT Inc.
1-26-2 Nishishinjuku, Shinjuku-ku, Tokyo 163-0515, Japan

*4Faculty of Engineering, Information and Systems, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

Received:
December 28, 2017
Accepted:
March 13, 2018
Published:
July 20, 2018
Keywords:
network clustering, cluster validity measures, modularity, k-medoids
Abstract
Cluster Validity Measures for Network Data

An illustrative example of the network data used in experiments.

Modularity is one of the evaluation measures for network partitions and is used as the merging criterion in the Louvain method. To construct useful cluster validity measures and clustering methods for network data, network cluster validity measures are proposed based on the traditional indices. The effectiveness of the proposed measures are compared and applied to determine the optimal number of clusters. The network cluster partitions of various network data which are generated from the Polaris dataset are obtained by k-medoids with Dijkstra’s algorithm and evaluated by the proposed measures as well as the modularity. Our numerical experiments show that the Dunn’s index and the Xie-Beni’s index-based measures are effective for network partitions compared to other indices.

Cite this article as:
Y. Hamasuna, D. Kobayashi, R. Ozaki, and Y. Endo, “Cluster Validity Measures for Network Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.4, pp. 544-550, 2018.
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Last updated on Dec. 13, 2018