Laboratory of Theoretical and Applied Computer Science, University of Lorraine, 57045 Metz, France.
Neural Comput. 2013 Oct;25(10):2776-807. doi: 10.1162/NECO_a_00490. Epub 2013 Jun 18.
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.
我们研究了凸函数（DC）规划和 DC 算法（DCA）在连续框架下解决块聚类问题的差异，传统上需要解决一个困难的组合优化问题。我们开发了 DC 规划的 DC 重述技术和精确罚函数，以构建块聚类问题的适当等价 DC 程序。它们为生成的 DC 程序提供了一个优雅而明确的 DCA 方案。计算实验表明了所提出算法的稳健性和效率，以及它优于标准算法，如双模 K-均值、双模模糊聚类和块分类 EM。
