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dc.contributor.authorKlawonn, Frank
dc.date.accessioned2007-10-05T09:13:18Z
dc.date.available2007-10-05T09:13:18Z
dc.date.issued2004
dc.identifier.issn1134-5632
dc.identifier.urihttp://hdl.handle.net/2099/3642
dc.description.abstractFuzzy clustering extends crisp clustering in the sense that objects can belong to various clusters with different membership degrees at the same time, whereas crisp or deterministic clustering assigns each object to a unique cluster. The standard approach to fuzzy clustering introduces the so-called fuzzifier which controls how much clusters may overlap. In this paper we illustrate, how this fuzzifier can help to reduce the number of undesired local minima of the objective function that is associated with fuzzy clustering. Apart from this advantage, the fuzzifier has also some drawbacks that are discussed in this paper. A deeper analysis of the fuzzifier concept leads us to a more general approach to fuzzy clustering that can overcome the problems caused by the fuzzifier.
dc.format.extent125-142
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.relation.ispartofMathware & soft computing . 2004 Vol. 11 Núm. 3
dc.rightsReconeixement-NoComercial-CompartirIgual 3.0 Espanya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.otherFuzzifier
dc.titleFuzzy clustering: insights and a new approach
dc.typeArticle
dc.subject.lemacAnàlisi multivariable
dc.subject.lemacCluster, Anàlisi de
dc.subject.amsClassificació AMS::62 Statistics::62H Multivariate analysis
dc.rights.accessOpen Access


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