Epsilon-Insensitive Fuzzy c-Medians Clustering

• Autorzy: Łęski, J.
• Języki publikacji: EN

Abstrakty

EN

Fuzzy clustering helps to find natural vague boundaries in data. The fuzzy C-Means (FCM) 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 sensivity to presence of noise and outliers in data. This paper introduces a new ε-insensitive Fuzzy C-Medians (εFCMed) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMed). Performance of the new clustering algorithm is experimentally compared with the Fuzzy C-Means (FCM) and the FCMed methods using synthetic heavy-tailed and overlapped groups in background noise, and the Iris database.

Słowa kluczowe

EN

fuzzy clustering; fuzzy c-medians; epsilon-intensivity; robust methods

• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2002
• Tom: Vol. 50, Nr 4
• Strony: 361--374
• Opis fizyczny: Bibliogr. 24 poz., 4 rys., 3 tab.

Twórcy

autor

Łęski, J.

• Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland (Instytut Elektroniki Politechniki Śląskiej)

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