DocumentCode :
3213733
Title :
Integrating rough clustering with Fuzzy sets
Author :
Revathy, S. ; Parvathavarthini, B.
Author_Institution :
Sathyabama Univ., Chennai, India
fYear :
2011
fDate :
20-22 July 2011
Firstpage :
865
Lastpage :
869
Abstract :
This paper presents the evolution and importance of clustering techniques, since clustering is unsupervised learning and there are many clustering methods in practice which results in which clustering scheme to be selected for our purpose .Here we take four clustering methodologies crisp Juzzy rough and rough fuzzy. These clustering methods have been implemented and its importance over one another is explained. And the suitable clustering method over these three has been identified for better perspective. The experiment results with the sample dataset illustrate the importance of clustering schemes.
Keywords :
fuzzy set theory; pattern clustering; rough set theory; unsupervised learning; Juzzy clustering methodology; crisp clustering methodology; fuzzy set; rough clustering; rough clustering methodology; rough fuzzy clustering methodology; unsupervised learning; Crisp clustering; Membership; Rough Fuzzy clustering; Rough clustering;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Sustainable Energy and Intelligent Systems (SEISCON 2011), International Conference on
Conference_Location :
Chennai
Type :
conf
DOI :
10.1049/cp.2011.0488
Filename :
6143437
Link To Document :
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