Paper Info
Title
Gaussian kernel minimum sum-of-squares clustering and solution method based on DCA
Abstract
In this paper, a Gaussian Kernel version of the Minimum Sum-of-Squares Clustering GKMSSC) is studied. The problem is formulated as a DC (Difference of Convex functions) program for which a new algorithm based on DC programming and DCA (DC Algorithm) is developed. The related DCA is original and very inexpensive. Numerical simulations show the efficiency of DCA and its superiority with respect to K-mean, a standard method for clustering.
Year
DOI
Venue
2012
10.1007/978-3-642-28490-8_35
ACIIDS
Keywords
Field
DocType
solution method,gaussian kernel version,related dca,gaussian kernel minimum,dc programming,new algorithm,dc algorithm,numerical simulation,convex function,standard method,minimum sum-of-squares clustering gkmssc
Computer science,Algorithm,Convex function,Artificial intelligence,Dc programming,Explained sum of squares,Cluster analysis,Gaussian function,Machine learning,Kernel (statistics)
Conference
Volume
ISSN
Citations 
7197
0302-9743
0
PageRank 
References 
Authors
0.34
16
3
Name
Order
Citations
PageRank
Le Hoai Minh11317.91
Le Thi Hoai An2103880.20
Pham Dinh Tao31340104.84