Linear mixture model approach for selecting fuzzy exponent value in fuzzy c-means algorithm

Article history: Received 31 July 2005 Received in revised form 27 September 2005 Accepted 5 October 2005 The implementations of both the supervised and unsupervised fuzzy c-means classification algorithms require a priori selection of the fuzzy exponent parameter. This parameter is a weighting exponent and it determines the degree of fuzziness of the membership grades. The determination of an optimal value for this parameter in a fuzzy classification process is problematic and remains an open problem. This paper presents a new and efficient procedure for determining a local optimal value for the fuzzy exponent in the implementation of fuzzy classification technique. Numerical results using simulated image and real data sets are used to illustrate the simplicity and effectiveness of the proposed method. © 2005 Elsevier B.V. All rights reserved.