DocumentCode
1391394
Title
Generalized fuzzy c-means clustering strategies using Lp norm distances
Author
Hathaway, Richard J. ; Bezdek, James C. ; Hu, Yingkang
Author_Institution
Dept. of Math. & Comput. Sci., Georgia Southern Univ., Statesboro, GA, USA
Volume
8
Issue
5
fYear
2000
fDate
10/1/2000 12:00:00 AM
Firstpage
576
Lastpage
582
Abstract
Fuzzy c-means (FCM) is a useful clustering technique. 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
Keywords
data models; fuzzy set theory; least squares approximations; matrix algebra; minimisation; pattern clustering; L1 norm distances; Lp norm distances; dissimilarity measure; generalized fuzzy c-means clustering strategies; object data; optimization schemes; relational data; robustness; semi-norm; Clustering algorithms; Computer science; Data models; Fuzzy sets; Helium; Least squares methods; Optimization methods; Partitioning algorithms; Prototypes; Robustness;
fLanguage
English
Journal_Title
Fuzzy Systems, IEEE Transactions on
Publisher
ieee
ISSN
1063-6706
Type
jour
DOI
10.1109/91.873580
Filename
873580
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