Generalized fuzzy c-means clustering strategies using Lp norm distances

Richard J. Hathaway, James C. Bezdek, Yingkang Hu

Research output: Contribution to journalArticlepeer-review

313 Scopus citations

Abstract

Fuzzy c-means (FCM) is a useful clustering technique. Recent modifications of FCM using L1 norm distances increase robustness to outliers. Object and relational data versions of FCM clustering are defined for the more general case where the Lp norm (p≥1) or semi-norm (0<p<1) is used as the measure of dissimilarity. We give simple (though computationally intensive) alternating optimization schemes for all object data cases of p>0 in order to facilitate the empirical examination of the object data models. Both object and relational approaches are included in a numerical study.

Original languageEnglish
Pages (from-to)576-582
Number of pages7
JournalIEEE Transactions on Fuzzy Systems
Volume8
Issue number5
DOIs
StatePublished - Oct 2000

Fingerprint

Dive into the research topics of 'Generalized fuzzy c-means clustering strategies using Lp norm distances'. Together they form a unique fingerprint.

Cite this