A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
A D. C. Optimization Algorithm for Solving the Trust-Region Subproblem
SIAM Journal on Optimization
Solving a Class of Linearly Constrained Indefinite QuadraticProblems by D.C. Algorithms
Journal of Global Optimization
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Leveraging the margin more carefully
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Trading convexity for scalability
ICML '06 Proceedings of the 23rd international conference on Machine learning
A new efficient algorithm based on DC programming and DCA for clustering
Journal of Global Optimization
Block clustering with Bernoulli mixture models: Comparison of different approaches
Computational Statistics & Data Analysis
Gene Selection for Cancer Classification Using DCA
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Exact penalty and error bounds in DC programming
Journal of Global Optimization
Learning sparse classifiers with difference of convex functions algorithms
Optimization Methods & Software - the 8th International Conference on Optimization: Techniques and Applications
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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.