Geodesic-dissimilarity-based hard and fuzzy c-means

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

研究成果: Paper

2 引用 (Scopus)

抜粋

In this paper, the geodesic distance is applied to relational clustering methods. First, it is shown that conventional methods are based on respective three types of relational clustering algorithms among nine ones, and the six rests of the nine ones with the geodesic distance are proposed. Second, geodesic dissimilarity is proposed by assigning the power of the Euclidean distance to the weight of the neighborhood graph of data. Numerical examples show that the proposed geodesicdissimilarity- based relational clustering algorithms successfully cluster the data that conventional squared-Euclidean-distancebased ones cannot.

元の言語English
ページ401-405
ページ数5
出版物ステータスPublished - 2010 12 1
イベントJoint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010 - Okayama, Japan
継続期間: 2010 12 82010 12 12

Conference

ConferenceJoint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010
Japan
Okayama
期間10/12/810/12/12

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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  • これを引用

    Kanzawa, Y., Endo, Y., & Miyamoto, S. (2010). Geodesic-dissimilarity-based hard and fuzzy c-means. 401-405. 論文発表場所 Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010, Okayama, Japan.