Geodesic-dissimilarity-based hard and fuzzy c-means

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages401-405
Number of pages5
Publication statusPublished - 2010 Dec 1
EventJoint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010 - Okayama, Japan
Duration: 2010 Dec 82010 Dec 12

Conference

ConferenceJoint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010
CountryJapan
CityOkayama
Period10/12/810/12/12

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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  • Cite this

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