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Towards a robust fuzzy clustering

✍ Scribed by Jacek Łęski

Publisher
Elsevier Science
Year
2003
Tongue
English
Weight
370 KB
Volume
137
Category
Article
ISSN
0165-0114

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✦ Synopsis

Fuzzy clustering helps to ÿnd 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 "-insensitive Fuzzy C-Means ("FCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMED). Also, methods with insensitivity control named FCM and ÿFCM 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.

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