Journal
FUZZY SETS AND SYSTEMS
Volume 137, Issue 2, Pages 215-233Publisher
ELSEVIER
DOI: 10.1016/S0165-0114(02)00372-X
Keywords
fuzzy clustering; fuzzy c-means; epsilon-insensitivity; robust methods
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Fuzzy clustering helps to find natural vague boundaries in data. The Fuzzy C-Means method (FCM) is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to presence of noise and outliers in data. This paper introduces a new epsilon-insensitive Fuzzy C-Means (epsilonFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMED). Also, methods with insensitivity control named alphaFCM and betaFCM are introduced. Performance of the new clustering algorithm is experimentally compared with the FCM method using synthetic data with outliers and heavy-tailed and overlapped groups of data in background noise. (C) 2002 Elsevier Science B.V. All rights reserved.
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