Computationally effective algorithm to the epsilon -insensitive fuzzy clustering

• Autorzy: Łęski J.
• Języki publikacji: EN

Abstrakty

EN

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 data. The epsilon -insensitive Fuzzy C-Means ( epsilon FCM) clustering algorithm is free of this disadvantage, but has a very high computational burden and requires a choice of the insensitivity parameter(s) epsilon In this paper, a new computationally effective epsilon -insensitive fuzzy c-means clustering algorithm with automatic adjustment of the insensitivity parameter(s) is introduced. Performance of the new clustering algorithm is experimentally verified using synthetic data with outliers and overlapped groups of heavy-tailed data.

Słowa kluczowe

EN

epsilon-insensitive fuzzy clustering; computationally effective algorithm; fuzzy c-means method; criterion function minimization; noise; outliers insensitivity parameter; automatic adjustment; synthetic data; data clustering; iterative linear programming; linear optimization

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2002
• Tom: Vol. 28, nr 3
• Strony: 31--50
• Opis fizyczny: Bibliogr. 19 poz.

Twórcy

autor

Łęski J.

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