Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/7260
Title: A method for training finite mixture models under a fuzzy clustering principle
Authors: Chatzis, Sotirios P. 
Keywords: Fuzzy systems;Computer science;Benchmarking (Management);Classification;Cluster analysis;Mixtures
Category: Electrical Engineering - Electronic Engineering - Information Engineering
Field: Engineering and Technology
Issue Date: 2010
Publisher: Elsevier
Source: Fuzzy sets and systems, 2010, vol. 161, no. 23, pp. 3000–3013
Abstract: In this paper, we establish a novel regard towards fuzzy clustering, showing it provides a sound framework for fitting finite mixture models. We propose a novel fuzzy clustering-type methodology for finite mixture model fitting, effected by utilizing a regularized form of the fuzzy c-means (FCM) algorithm, and introducing a proper dissimilarity functional for the algorithm with respect to the probabilistic properties of the model being treated. We apply the proposed methodology in a number of popular finite mixture models, and the corresponding expressions of the fuzzy model fitting algorithm are derived. We examine the efficacy of our novel approach in both clustering and classification applications of benchmark data sets, and we demonstrate the advantages of the proposed approach over maximum-likelihood
URI: http://ktisis.cut.ac.cy/handle/10488/7260
ISSN: 0165-0114
DOI: 10.1016/j.fss.2010.03.015
Rights: © 2010 Elsevier. All rights reserved
Type: Article
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