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JACIII Vol.20 No.4 pp. 561-570
doi: 10.20965/jaciii.2016.p0561
(2016)

Paper:

Power-Regularized Fuzzy c-Means Clustering with a Fuzzification Parameter Less Than One

Yuchi Kanzawa

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

Received:
January 3, 2016
Accepted:
April 13, 2016
Online released:
July 19, 2016
Published:
July 19, 2016
Keywords:
fuzzy clustering, power regularization
Abstract

The present study proposes two types of power-regularized fuzzy c-means (pFCM) clustering algorithms with a fuzzification parameter less than one, which supplements previous work on pFCM with a fuzzification parameter greater than one. Both the proposed methods are essentially identical to each other, but not when fuzzification parameter values are specified. Theoretical discussion reveals the property of the proposed methods, and some numerical results substantiate the property of the proposed methods and show that the proposed methods outperform two conventional methods from an accuracy point of view.

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Last updated on Apr. 21, 2017