DocumentCode
2279517
Title
Clustering of fuzzy data using credibilistic critical values
Author
Sampath, Smita ; Kalaivani, R.
Author_Institution
Dept. of Stat., Univ. of Madras, Chennai, India
fYear
2010
fDate
15-17 Dec. 2010
Firstpage
227
Lastpage
232
Abstract
In this paper, the usage of credibilistic critical values in the implementation of k -means and k-medoids algorithm has been explored in clustering of fuzzy data set. An illustrative numerical example based on a bench mark data set is given to demonstrate the study. Also the performances of the two algorithms when used with critical values have been compared in terms of various cluster validity measures.
Keywords
fuzzy set theory; pattern clustering; statistical analysis; credibilistic critical values; fuzzy data clustering; k-means algorithm; k-medoids algorithm; Clustering algorithms; Entropy; Object recognition; Partitioning algorithms; Pragmatics; Temperature measurement; Clustering; Credibility space; Entropy; F measure; Fuzzy variable; Precision; Purity; Recall;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal and Image Processing (ICSIP), 2010 International Conference on
Conference_Location
Chennai
Print_ISBN
978-1-4244-8595-6
Type
conf
DOI
10.1109/ICSIP.2010.5697474
Filename
5697474
Link To Document