Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/7250
Title: Robust fuzzy clustering using mixtures of Student’s-t distributions
Authors: Chatzis, Sotirios P. 
Varvarigou, Theodora 
Keywords: Pattern recognition;Fuzzy systems;Algorithms;Mixtures;Students
Category: Electrical Engineering - Electronic Engineering - Information Engineering
Field: Engineering and Technology
Issue Date: Oct-2008
Publisher: Elsevier
Source: Pattern recognition letters, 2008, vol. 29, no. 13, pp. 1901–1905
Journal: Pattern Recognition Letters 
Abstract: In this paper, we propose a robust fuzzy clustering algorithm, based on a fuzzy treatment of finite mixtures of multivariate Student’s-t distributions, using the fuzzy c-means (FCM) algorithm. As we experimentally demonstrate, the proposed algorithm, by incorporating the assumptions about the probabilistic nature of the clusters being dirived into the fuzzy clustering procedure, allows for the exploitation of the hard tails of the multivariate Student’s-t distribution, to obtain a robust to outliers fuzzy clustering algorithm, offering increased clustering performance comparing to existing FCM-based algorithms. Our experimental results prove that the proposed fuzzy treatment of finite mixtures of Student’s-t distributions is more effective comparing to their statistical treatments using EM-type algorithms, while imposing comparable computational loads
URI: http://ktisis.cut.ac.cy/handle/10488/7250
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2008.06.013
Rights: © 2008 Elsevier. All rights reserved
Type: Article
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