An varepsilon-Insensitive Approach to Fuzzy Clustering

• Autorzy: Łęski J.
• 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 varepsilon-insensitive Fuzzy C-Means (varepsilonFCM) 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 clustering; fuzzy c-means; robust methods; varepsilon-insensitivity; fuzzy c-medians

PL

programowanie; metoda grupowania

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2001
• Tom: Vol. 11, no 4
• Strony: 993--1007
• Opis fizyczny: Bibliogr. 16 poz., tab., wykr.

Twórcy

autor

Łęski J.

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