288 pp. per issue
6 x 9, illustrated
2014 Impact factor:

Neural Computation

October 2013, Vol. 25, No. 10, Pages 2776-2807
(doi: 10.1162/NECO_a_00490)
© 2013 Massachusetts Institute of Technology
Block Clustering Based on Difference of Convex Functions (DC) Programming and DC Algorithms
Article PDF (232.17 KB)

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.