Cluster Validity Measures for Network Data
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- Hamasuna Yukihiro
- Department of Informatics, School of Science and Engineering, Kindai University
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- Kobayashi Daiki
- Graduate School of Science and Engineering, Kindai University
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- Ozaki Ryo
- ALBERT Inc.
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- Endo Yasunori
- Faculty of Engineering, Information and Systems, University of Tsukuba
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Abstract
<p>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.</p>
Journal
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- Journal of Advanced Computational Intelligence and Intelligent Informatics
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Journal of Advanced Computational Intelligence and Intelligent Informatics 22 (4), 544-550, 2018-07-20
Fuji Technology Press Ltd.
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Details 詳細情報について
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- CRID
- 1390564237999083008
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- NII Article ID
- 130007402380
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- NII Book ID
- AA12042502
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- ISSN
- 18838014
- 13430130
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- NDL BIB ID
- 029097447
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed
