Soft clustering: Fuzzy and rough approaches and their extensions and derivatives

Journal article


Peters, Georg, Crespo, Fernando, Lingas, Pawan and Weber, Richard. (2013) Soft clustering: Fuzzy and rough approaches and their extensions and derivatives. International Journal of Approximate Reasoning. 54(2), pp. 307 - 322. https://doi.org/10.1016/j.ijar.2012.10.003
AuthorsPeters, Georg, Crespo, Fernando, Lingas, Pawan and Weber, Richard
Abstract

Clustering is one of the most widely used approaches in data mining with real life applications in virtually any domain. The huge interest in clustering has led to a possibly three-digit number of algorithms with the k-means family probably the most widely used group of methods. Besides classic bivalent approaches, clustering algorithms belonging to the domain of soft computing have been proposed and successfully applied in the past four decades. Bezdek’s fuzzy c-means is a prominent example for such soft computing cluster algorithms with many effective real life applications. More recently, Lingras and West enriched this area by introducing rough k-means. In this article we compare k-means to fuzzy c-means and rough k-means as important representatives of soft clustering. On the basis of this comparison, we then survey important extensions and derivatives of these algorithms; our particular interest here is on hybrid clustering, merging fuzzy and rough concepts. We also give some examples where k-means, rough k-means, and fuzzy c-means have been used in studies.

Year2013
JournalInternational Journal of Approximate Reasoning
Journal citation54 (2), pp. 307 - 322
PublisherElsevier Inc.
ISSN0888-613X
Digital Object Identifier (DOI)https://doi.org/10.1016/j.ijar.2012.10.003
Scopus EID2-s2.0-84873284300
Page range307 - 322
Research GroupSchool of Arts
Place of publicationUnited States of America
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https://acuresearchbank.acu.edu.au/item/85v6x/soft-clustering-fuzzy-and-rough-approaches-and-their-extensions-and-derivatives

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