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
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;
Conference_Titel :
Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
0-7803-3645-3
DOI :
10.1109/FUZZY.1996.552379
