Robust weighted fuzzy c-means clustering

Nowadays, the fuzzy c-means method (FCM) became one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm ca...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Hadjahmadi, A.H., Homayounpour, M.M., Ahadi, S.M.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Nowadays, the fuzzy c-means method (FCM) became one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called robust weighted fuzzy c-means (RWFCM). We used a new objective function that uses some kinds of weights for reducing the infection of noises in clustering. Experimental results show that compared to three well-known clustering algorithms, namely, the fuzzy possibilistic c-means (FPCM), credibilistic fuzzy c-means (CFCM) and density weighted fuzzy c-means (DWFCM), RWFCM is less sensitive to outlier and noise and has an acceptable computational complexity.
ISSN:1098-7584
DOI:10.1109/FUZZY.2008.4630382