DocumentCode :
3573265
Title :
The graded possibilistic clustering model
Author :
Masulli, Francesco ; Rovetta, Stefano
Author_Institution :
Nat. Inst. for the Phys. of Matter, INFM, Italy
Volume :
1
fYear :
2003
Firstpage :
791
Abstract :
This paper presents the graded possibilistic model. After reviewing some clustering algorithms derived from c-Means, we provide a unified perspective on these clustering algorithms, focused on the memberships rather than on the cost function. Then the concept of graded possibility is introduced. This is a partially possibilistic version of the fuzzy clustering model, as compared to Krishnapuram and Keller´s possibilistic clustering. We outline a basic graded possibilistic clustering algorithm and highlight the different properties attainable by means of experimental demonstrations.
Keywords :
fuzzy set theory; pattern clustering; self-organising feature maps; vector quantisation; Keller possibilistic clustering; Krishnapuram possibilistic clustering; c-Means; clustering algorithms; cost function; fuzzy clustering model; graded possibilistic clustering model; partially possibilistic version; Annealing; Clustering algorithms; Computer science; Cost function; Electronic mail; Membership renewal; Neural networks; Partitioning algorithms; Physics computing; Self organizing feature maps;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN :
1098-7576
Print_ISBN :
0-7803-7898-9
Type :
conf
DOI :
10.1109/IJCNN.2003.1223483
Filename :
1223483
Link To Document :
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