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JACIII Vol.13 No.4 pp. 429-433
doi: 10.20965/jaciii.2009.p0429
(2009)

Paper:

Clustering Algorithm Based on Probabilistic Dissimilarity

Makito Yamashiro*, Yasunori Endo**, and Yukihiro Hamasuna*

*Graduate School of Systems and Information Engineering, University of Tsukuba

**Department of Risk Engineering, Faculty of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

Received:
November 28, 2008
Accepted:
March 10, 2009
Published:
July 20, 2009
Keywords:
probabilistic dissimilarity, clustering, optimization
Abstract

The clustering algorithm we propose is based on probabilistic dissimilarity, which is formed by introducing the concept of probability into conventional dissimilarity. After defining probabilistic dissimilarity, we present examples of probabilistic dissimilarity functions. After considering an objective function with probabilistic dissimilarity. Furthermore, we construct a clustering algorithm probabilistic dissimilarity based using optimal solutions maximizing the objective function. Numerical examples verify the effectiveness of our algorithm.

Cite this article as:
Makito Yamashiro, Yasunori Endo, and Yukihiro Hamasuna, “Clustering Algorithm Based on Probabilistic Dissimilarity,” J. Adv. Comput. Intell. Intell. Inform., Vol.13, No.4, pp. 429-433, 2009.
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References
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