DocumentCode
304094
Title
A clustering algorithm based on minimum volume
Author
Krishnapuram, Raghu ; Kim, Jongwoo
Author_Institution
Dept. of Comput. Eng. & Comput. Sci., Missouri Univ., Columbia, MO, USA
Volume
2
fYear
1996
fDate
8-11 Sep 1996
Firstpage
1387
Abstract
Most fuzzy clustering algorithms are derived from the fuzzy C-means (FCM) algorithm, which minimizes the sum of squared distances from the prototypes weighted by the corresponding memberships. In this paper, we consider a new clustering algorithm based on the minimization of the sum of the volumes of the clusters. The performance of the algorithm is shown to be better than that of the traditional algorithms when the data set contains clusters of widely varying sizes, shapes, and densities
Keywords
covariance matrices; fuzzy set theory; minimisation; pattern recognition; fuzzy clustering algorithms; memberships; minimum volume clustering algorithm; Automatic frequency control; Clustering algorithms; Computer science; Covariance matrix; Design engineering; Equations; Maximum likelihood estimation; Minimization methods; Prototypes; Shape;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on
Conference_Location
New Orleans, LA
Print_ISBN
0-7803-3645-3
Type
conf
DOI
10.1109/FUZZY.1996.552379
Filename
552379
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