Block clustering based on difference of convex functions (DC) programming and DC algorithms

We investigate difference of convex functions (DC) programming and the DC algorithm (DCA) to solve the block clustering problem in the continuous framework, which traditionally requires solving a hard combinatorial optimization problem. DC reformulation techniques and exact penalty in DC programming...

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Veröffentlicht in:Neural computation Jg. 25; H. 10; S. 2776
Hauptverfasser: Le, Hoai Minh, Le Thi, Hoai An, Dinh, Tao Pham, Huynh, Van Ngai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.10.2013
Schlagworte:
Algorithms
Artificial Intelligence
Brain Neoplasms - pathology
Cluster Analysis
Computer Simulation
Databases, Factual
Fuzzy Logic
Humans
Lung Neoplasms - pathology
Neoplasms - pathology
Problem Solving
Software
ISSN:1530-888X, 1530-888X
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Zusammenfassung:We investigate difference of convex functions (DC) programming and the DC algorithm (DCA) to solve the block clustering problem in the continuous framework, which traditionally requires solving a hard combinatorial optimization problem. DC reformulation techniques and exact penalty in DC programming are developed to build an appropriate equivalent DC program of the block clustering problem. They lead to an elegant and explicit DCA scheme for the resulting DC program. Computational experiments show the robustness and efficiency of the proposed algorithm and its superiority over standard algorithms such as two-mode K-means, two-mode fuzzy clustering, and block classification EM.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1530-888X
1530-888X
DOI:10.1162/NECO_a_00490