Sequential Cluster Extraction Using Power-Regularized Possibilistic c-Means

Yuchi Kanzawa

Shibaura Institute of Technology, 3-7-5 Toyosu, Koto, Tokyo 135-8548, Japan

Received:

April 20, 2014

Accepted:

August 25, 2014

Published:

January 20, 2015

Keywords:

sequential cluster extraction, possibilistic clustering, power-regularization

Abstract

The present study proposes an algorithm for sequential cluster extraction using power-regularized possibilistic c-means (pPCM). First, pPCM is derived in a similar manner to two types of entropy-regularized possibilistic c-means (ePCM) derivations, where a power function is utilized instead of the negative entropy in ePCM. The cluster fusion with pPCM is identical to the mean-shift with a generalized Epanichnikov kernel, whereas the proposed method employs sequential cluster extraction with pPCM. Numerical examples show that the cluster number produced by the proposed algorithm did not match with the true class number in real datasets, but the extracted clustering results were partially successful in terms of capturing dense regions of objects.

Cite this article as:

Y. Kanzawa, “Sequential Cluster Extraction Using Power-Regularized Possibilistic c-Means,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.1, pp. 67-73, 2015.

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