Clustering Algorithm Based on Probabilistic Dissimilarity

Makito Yamashiro; Yasunori Endo; Yukihiro Hamasuna

doi:10.20965/jaciii.2009.p0429

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JACIII Vol.13 No.4 pp. 429-433

doi: 10.20965/jaciii.2009.p0429

(2009)

Paper:

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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:

M. Yamashiro, Y. Endo, and Y. 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

[1] J. C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms,” Plenum, New York, 1991.
[2] K. Jajuga, “Classification, Clustering, and Data Analysis: Recent Advances and Applications,” Springer, 2002.
[3] B. Schweizer and A. Solar, “Probabilistic Metric Spaces,” Dover Publications, Inc. Mineola, New York, 2005.
[4] O. Hadzic and A. Pap, “Fixed Point Theory in Probabilistic metric Spaces,” KLUWER ACADEMIC PUBLISHERS, 2001.
[5] K. Menger, “Statistical metrics,” Proc. Nat Acad. of Sci., U.S.A., 28, pp. 535-537, 1942.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] J. C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms,” Plenum, New York, 1991.

[2] [2] K. Jajuga, “Classification, Clustering, and Data Analysis: Recent Advances and Applications,” Springer, 2002.

[3] [3] B. Schweizer and A. Solar, “Probabilistic Metric Spaces,” Dover Publications, Inc. Mineola, New York, 2005.

[4] [4] O. Hadzic and A. Pap, “Fixed Point Theory in Probabilistic metric Spaces,” KLUWER ACADEMIC PUBLISHERS, 2001.

[5] [5] K. Menger, “Statistical metrics,” Proc. Nat Acad. of Sci., U.S.A., 28, pp. 535-537, 1942.

Clustering Algorithm Based on Probabilistic Dissimilarity

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

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