DocumentCode :
3486731
Title :
Hierarchical clustering to validate fuzzy clustering
Author :
Delgado, M. ; Gomez-Skarmeta, A. ; Vila, M.A.
Author_Institution :
Dept. de Ciencias de la Comput. e Inteligencia Artificial, Granada Univ., Spain
Volume :
4
fYear :
1995
fDate :
20-24 Mar 1995
Firstpage :
1807
Abstract :
Fuzzy clustering is now extensively used for identification of (fuzzy) systems. Starting from a set of examples (input-output pairs) of a certain system, fuzzy clustering permits to disclose fuzzy rules governing the given system and also to make direct inference from new observations of the input. Our proposal in this paper attempts to present an approach to the problem of validating fuzzy clustering processes. The cluster method before the fuzzy clustering, in order to select a suitable initial structure. With this objective, several consistence measures for crisp classifications are introduced. Using these measures on the hierarchy of classifications associated to an hierarchical cluster the most suitable level is obtained. From this classification the fuzzy clustering process is started
Keywords :
fuzzy set theory; hierarchical systems; identification; inference mechanisms; pattern recognition; consistence measures; fuzzy clustering; fuzzy rules; hierarchical clustering; identification; inference; similarity relations; validation; Clustering algorithms; Data analysis; Fuzzy sets; Fuzzy systems; Information analysis; Loss measurement; Partitioning algorithms; Proposals; Stability; Supervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 1995. International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium., Proceedings of 1995 IEEE Int
Conference_Location :
Yokohama
Print_ISBN :
0-7803-2461-7
Type :
conf
DOI :
10.1109/FUZZY.1995.409926
Filename :
409926
Link To Document :
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