Robust fuzzy clustering using mixtures of Student's-t distributions

Chatzis, S; Varvarigou, T

dc.contributor.author	Chatzis, S	en
dc.contributor.author	Varvarigou, T	en
dc.date.accessioned	2014-03-01T01:29:06Z
dc.date.available	2014-03-01T01:29:06Z
dc.date.issued	2008	en
dc.identifier.issn	0167-8655	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19129
dc.subject	Finite mixture models	en
dc.subject	Fuzzy c-means	en
dc.subject	Fuzzy clustering	en
dc.subject	Student's-t distributions	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	Algorithms	en
dc.subject.other	Arsenic compounds	en
dc.subject.other	Boolean functions	en
dc.subject.other	Clustering algorithms	en
dc.subject.other	Flow of solids	en
dc.subject.other	Fuzzy logic	en
dc.subject.other	Fuzzy rules	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Fuzzy systems	en
dc.subject.other	Image segmentation	en
dc.subject.other	Mixtures	en
dc.subject.other	Probability distributions	en
dc.subject.other	Students	en
dc.subject.other	Clustering performance	en
dc.subject.other	Clustering procedures	en
dc.subject.other	Computational loads	en
dc.subject.other	Finite mixtures	en
dc.subject.other	Fuzzy c means (FCM) algorithms	en
dc.subject.other	Fuzzy Clustering algorithms	en
dc.subject.other	Robust fuzzy clustering	en
dc.subject.other	T distributions	en
dc.subject.other	Fuzzy clustering	en
dc.title	Robust fuzzy clustering using mixtures of Student's-t distributions	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.patrec.2008.06.013	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.patrec.2008.06.013	en
heal.language	English	en
heal.publicationDate	2008	en
heal.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. (C) 2008 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Pattern Recognition Letters	en
dc.identifier.doi	10.1016/j.patrec.2008.06.013	en
dc.identifier.isi	ISI:000258817900019	en
dc.identifier.volume	29	en
dc.identifier.issue	13	en
dc.identifier.spage	1901	en
dc.identifier.epage	1905	en