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Warianty tytułu
Języki publikacji
Abstrakty
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.
Rocznik
Tom
Strony
993--1007
Opis fizyczny
Bibliogr. 16 poz., tab., wykr.
Twórcy
autor
- Institute of Electronics Silesian University of Technology Akademicka 16, 44-100 Gliwice, Poland, jl@boss.iele.polsl.gliwice.pl
Bibliografia
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ1-0012-0047