Block clustering based on difference of convex functions (DC) programming and DC algorithms
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Updated: May 10, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Hoai Minh Le1, Hoai An Le Thi, Tao Pham Dinh
1Laboratory of Theoretical and Applied Computer Science, University of Lorraine, 57045 Metz, France. minh.le@univ-lorraine.fr
Abstract:
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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