Title :
Possibilistic c-means clustering using fuzzy relations
Author :
Zarandi, M.H.F. ; Kalhori, M. Rostam Niakan ; Jahromi, M.F.
Author_Institution :
Dept. of Ind. Eng., Univ. of Amirkabir (Tehran Ploythechnic), Tehran, Iran
Abstract :
The aim of this paper is designing a new approach for objective function- based fuzzy clustering. A new algorithm will be proposed for possibilistic c-means (PCM)-based models. This PCM-based algorithm uses fuzzy relations. In order to consider both separation between clusters and compactness within clusters, fuzzy relations will be applied. For verifying the efficiency of the proposed algorithm, experimental tests will be implemented.
Keywords :
fuzzy set theory; pattern clustering; PCM-based algorithm; fuzzy relations; objective function-based fuzzy clustering; possibilistic c-means clustering; Algorithm design and analysis; Clustering algorithms; Clustering methods; Data models; Linear programming; Partitioning algorithms; Phase change materials; fuzzy relations; objective function- based clustering; possibilistic clustering;
Conference_Titel :
IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint
Conference_Location :
Edmonton, AB
DOI :
10.1109/IFSA-NAFIPS.2013.6608560
