title: Block clustering based on difference of convex functions (DC) programming and DC algorithms.
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.
authors.affiliation: Laboratory of Theoretical and Applied Computer Science, University of Lorraine, 57045 Metz, France. minh.le@univ-lorraine.fr
authors.forename: Hoai Minh
authors.initials: HM
authors.lastname: Le
authors.forename: Hoai An
authors.initials: HA
authors.lastname: Le Thi
authors.forename: Tao Pham
authors.initials: TP
authors.lastname: Dinh
authors.forename: Van Ngai
authors.initials: VN
authors.lastname: Huynh
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001932:Brain Neoplasms / Q000473:pathology; D016000:Cluster Analysis; D003198:Computer Simulation; D016208:Databases, Factual; D017143:Fuzzy Logic; D006801:Humans; D008175:Lung Neoplasms / Q000473:pathology; D009369:Neoplasms / Q000473:pathology; D011340:Problem Solving; D012984:Software