An ε-insensitive approach to fuzzy clustering

• Autorzy: Jacek Łęski
• Języki publikacji: EN

Abstrakty

EN

Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-means method 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 the presence of noise and outliers in the data. The present paper introduces a new ε-insensitive Fuzzy C-Means (εFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMED). The performance of the new clustering algorithm is experimentally compared with the Fuzzy C-Means (FCM) method using synthetic data with outliers and heavy-tailed, overlapped groups of the data.

Słowa kluczowe

EN

fuzzy c-medians; ε-insensitivity; robust methods; fuzzy c-means; fuzzy clustering

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2001
• Tom: 11
• Numer: 4
• Strony: 993-1007

Daty

wydano

2001

otrzymano

2001-03-30

poprawiono

2001-07-04

Twórcy

autor

Jacek Łęski

• Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland

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