Cluster Validity Measures for Network Data

• Hamasuna Yukihiro

  Department of Informatics, School of Science and Engineering, Kindai University

• Kobayashi Daiki

  Graduate School of Science and Engineering, Kindai University

• Ozaki Ryo

  ALBERT Inc.

• Endo Yasunori

  Faculty of Engineering, Information and Systems, University of Tsukuba

抄録

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.

収録刊行物

• Journal of Advanced Computational Intelligence and Intelligent Informatics

  Journal of Advanced Computational Intelligence and Intelligent Informatics 22 (4), 544-550, 2018-07-20

  富士技術出版株式会社

キーワード

• network clustering
• cluster validity measures
• modularity
• <i>k</i>-medoids

詳細情報

• CRID: 1390564237999083008
• NII論文ID: 130007402380
• NII書誌ID: AA12042502
• DOI: 10.20965/jaciii.2018.p0544
• ISSN: 18838014
• ISSN: 13430130
• NDL書誌ID: 029097447
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I029097447
• Web Site: https://www.fujipress.jp/main/wp-content/themes/Fujipress/phyosetsu.php?ppno=JACII002200040015
• 本文言語コード: en
• データソース種別:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可