New and efficient DCA based algorithms for minimum sum-of-squares clustering

The purpose of this paper is to develop new efficient approaches based on DC (Difference of Convex functions) programming and DCA (DC Algorithm) to perform clustering via minimum sum-of-squares Euclidean distance. We consider the two most widely used models for the so-called Minimum Sum-of-Squares C...

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Veröffentlicht in:	Pattern recognition Jg. 47; H. 1; S. 388 - 401
Hauptverfasser:	Hoai An, Le Thi, Hoai Minh, Le, Tao, Pham Dinh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.01.2014 Elsevier
Schlagworte:	Algorithms Applied sciences Clustering Combinatorial optimization Computer Science DC programming DCA Direct current Exact penalty Exact sciences and technology Gaussian Gaussian kernel Information, signal and communications theory Kernels Mathematical models Mixed integer MSSC Nonsmooth nonconvex programming Programming Signal and communications theory Signal representation. Spectral analysis Signal, noise Telecommunications and information theory MSSC Gaussian kernel DCA Exact penalty Nonsmooth nonconvex programming DC programming Clustering Combinatorial optimization Performance evaluation Automatic classification Scalability Iterative method Mixed integer programming K means algorithm Signal classification Convex programming Kernel method Regularization method Algorithm performance Numerical simulation Convex function
ISSN:	0031-3203, 1873-5142
Online-Zugang:	Volltext
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