Collaborative DCA: An intelligent collective optimization scheme, and its application for clustering

Issue title: Special Section: Collective intelligence in information systems

Guest editors: Ngoc Thanh Nguyen, Edward Szczerbicki, Bogdan Trawiński and Van Du Nguyen

Article type: Research Article

Authors: Le Thi, Hoai An^{a; b; c; *}

Affiliations: [a] Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam | [b] Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam | [c] Université de Lorraine, LGIPM, F-57000 Metz, France

Correspondence: [*] Corresponding author. Hoai An Le Thi, E-mail: [email protected].

Abstract: This paper deals with a new and efficient collective optimization approach, based on DC (Difference of Convex functions) programming and DCA (DC Algorithm), powerful tools of nonconvex programming. Exploiting the efficiency and the flexibility of DCA we develop the so-called collaborative DCA in which divers DCA based algorithms are cooperated in an effective way. Two versions of collaborative DCA are proposed and their applications on clustering, a fundamental problem in unsupervised learning, are studied. Numerical experiments are performed on several datasets. The comparative results with three DCA component algorithms show that the collaborative DCA outperforms them on quality and it realizes a good trade-off between the quality of solutions and the running time.

Keywords: Collective optimization, DC programming, DCA, Collaborative DCA, Clustering.

DOI: 10.3233/JIFS-179358

Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7511-7518, 2019