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