Evolutionary multi-objective clustering for overlapping clusters detection

Authors:
Kazi Shah Nawaz Ripon;M. N. H. Siddique
Affiliations:
Computer Science and Engineering, Khulna University, Bangladesh and Department of Informatics, University of Oslo, Norway;School of Computing and Intelligent Systems, University of Ulster, United Kingdom
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

Evolutionary algorithms have a history of being applied into clustering analysis. However, most of the existing evolutionary clustering techniques fail to detect complex/spiral shaped clusters. In our previous works, we proposed several evolutionary multi-objective clustering algorithms and achieved promising results. Still, they suffer from this usual problem exhibited by evolutionary and unsupervised clustering approaches. In this paper, we proposed an improved multi-objective evolutionary clustering approach (EMCOC) to resolve the overlapping problems in complex shape data. Experimental results based on several artificial and real-world data show that the proposed EMCOC can successfully identify overlapping clusters. It also succeeds obtaining nondominated and near-optimal clustering solutions in terms of different cluster quality measures and classification performance. The superiority of the EMCOC over some other multi-objective evolutionary clustering algorithms is also confirmed by the experimental results.