Two-Stage Clustering Based on Cluster Validity Measures

• Hamasuna Yukihiro

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

• Ozaki Ryo

  Graduate School of Science and Engineering, Kindai University

• Endo Yasunori

  Faculty of Engineering, Information and Systems, University of Tsukuba

抄録

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 W_tr and its kernelization KW_tr are quite effective when applying the proposed method, and obtain better results than the other indices.

収録刊行物

• Journal of Advanced Computational Intelligence and Intelligent Informatics

  Journal of Advanced Computational Intelligence and Intelligent Informatics 22 (1), 54-61, 2018-01-20

  富士技術出版株式会社

キーワード

• two-stage clustering
• cluster validity measures
• kernel method
• <i>c</i>-means clustering
• agglomerative hierarchical clustering

詳細情報

• CRID: 1390564238027184000
• NII論文ID: 130007492659
• NII書誌ID: AA12042502
• DOI: 10.20965/jaciii.2018.p0054
• ISSN: 18838014
• ISSN: 13430130
• NDL書誌ID: 028764951
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I028764951
• Web Site: https://www.fujipress.jp/main/wp-content/themes/Fujipress/phyosetsu.php?ppno=JACII002200010006
• 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