DocumentCode
3168043
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
fYear
2013
fDate
24-28 June 2013
Firstpage
1137
Lastpage
1142
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;
fLanguage
English
Publisher
ieee
Conference_Titel
IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint
Conference_Location
Edmonton, AB
Type
conf
DOI
10.1109/IFSA-NAFIPS.2013.6608560
Filename
6608560
Link To Document