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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Bibliographic Details
Published in:Neural computation Vol. 25; no. 10; p. 2776
Main Authors: Le, Hoai Minh, Le Thi, Hoai An, Dinh, Tao Pham, Huynh, Van Ngai
Format: Journal Article
Language:English
Published: United States 01.10.2013
Subjects:
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
Online Access:Get more information
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Summary: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.
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ISSN:1530-888X
1530-888X
DOI:10.1162/NECO_a_00490