Identification of overlapping clusters in complex data has been remaining as the problem to tackle. To the best knowledge, no evolutionary and unsupervised clustering approach is able to detect it successfully. Most of the existing evolutionary clustering techniques fail to detect complex/spiral shaped clusters. This research adopts an optimization method called Ant Colony Optimization (ACO) with the existing algorithm called Evolutionary Multi-objective Clustering (EMC) for overlapping clusters detection. This work resolves the problem of overlapping clusters by enhancing the multi-objective evolutionary clustering approach with Genetic Algorithm (GA) with variable length chromosome & local search for feature selection. Combined with Evolutionary Multiobjective Clustering, Ant Colony Optimization (ACOEMCOC) approach succeeds in obtaining non-dominated and near-optimal clustering solutions in terms of different cluster quality measures like purity, and index etc., and classification performance.

Evolutionary Multiobjective Clustering (EMC), EMCOC, FEMCOC, Genetic Algorithm (GA), Ant Colony Optimization (ACO) algorithm

[1]. K.P Malarkodi, S.Punithavathy, "A Fuzzy Based Evolutionary Multi-objective Clustering for overlapping Clusters Detection" IJSER Vol 2, Issue 9, Sept 2011. ISSN 2229-5518.
[2]. E. Falkenauer. Genetic Algorithms and Grouping Problems. John Wiley & Sons,1998
[3]. U. Maulik and S. Bandyopadhyay. Genetic algorithm-based clustering technique. Pattern Recognition, 33:1455{1465, 2000.
[4]. K. Deb. Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester, UK,
[5].L. MacQueen. Some methods for classification and analysis of multivariate observations.In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, volume 1, pages 281-297. University of California Press, Berkeley, 1967.
[6]. D. W. Corne, N. R. Jerram, J. D. Knowles, and M. J. Oates. PESA-II: Region-based Selection in Evolutionary Multiobjective Optimization.In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO`2001), pages 283 to 290. Morgan Kaufmann Publishers, 2001.
[7]. D. W. Corne, J. D. Knowles, and M. J. Oates. The Pareto Envelope-based Selection Algorithm for Multiobjective Optimization. In Proceedings of the Parallel Problem Solving from Nature VI Conference, pages 839-848. Springer,2000.
[8]. N. J. Radcli_e. Equivalence class analysis of genetic algorithms. Complex Systems, 5:183 {205, 1991}.
[9].A. Topchy, A. K. Jain, and W. Punch. A mixture model for clustering ensembles.In Proceedings SIAM Conf. on Data Mining, 2004.
[10].J.D. Schaffer, Multiple Objective Optimization with Vector Evaluated Genetic Algorithms, Ph.D. Thesis, Vanderbilt University, Nashville, TN, 1984.
[11]. A. Topchy, A. K. Jain, W. Punch, "Clustering ensembles: models of consensus and weak partitions,"IEEE Intelligence, vol. 27, no. 2, pp. 1866- 1881, 2005
[12]. R.Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications, In Proceedings of ACM-SIGMOD 1998 Int. Conf. on Management of Data: 94-105, 1998.
[13]. C.-L.Hwang and A.S.Masud. Multiple Objective Decision Making—Methods and Applications. Springer- Verlag, Heidelberg, 1979.
[14]. K. S. N. Ripon, "Real jumping gene genetic algorithm (RJGGA) for evolutionary multi-objective optimization problems," M.Phil Thesis, Department of Computer Science, City University of Hong Kong, Hong Kong, 2006
[15]. K. S. N. Ripon, C. –H. Tsang, and S. Kwong, "Multi-objective data clustering using variable-length real jumping genes genetic algorithm and local search method," in Proc. International Joint Conference on Neural Networks (ICJNN`06), Vancouver, Canada, 2006, pp. 3609-3616.
[16]. J. Handl, and J. Knowles, "Evolutionary multi-objective clustering," in Proc. Eighth Int. Conf. on Parallel Problem Solving from Nature, 2004, pp. 1081-1091.
[17]. Ajith Abraham and Lakhmi Jain, Evolutionary Multiobjective Optimization, Oklahoma State University, USA, 2007
[18]. M. Dorigo, V. Maniezzo, et A. Colorni, Ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics--Part B , volume 26, numéro 1, pages 29-41, 1996.
[19]. A. Colorni, M. Dorigo et V. Maniezzo, Distributed Optimization by Ant Colonies, acts de la première conférence européenne sur la vie artificielle, Paris, France, Elsevier Publishing, 134-142, 1991.
[20]. S. Goss, S. Aron, J.-L. Deneubourg et J.-M. Pasteels, The self-organized exploratory pattern of the Argentine ant, Naturwissenschaften, volume 76, pages 579-581, 1989.
[21]. J.-L. Deneubourg, S. Aron, S. Goss et J.-M. Pasteels, The self-organizing exploratory pattern of the Argentine ant, Journal of Insect Behavior, volume 3, page 159, 1990.
[22]. M. Zlochin, M. Birattari, N. Meuleau, et M. Dorigo, Model-based search for combinatorial optimization: A critical survey, Annals of Operations Research, vol. 131, pp. 373-395, 2004
[23]. M. Dorigo and L. M. Gambardella, "Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem", IEEE Transactions on Evolutionary Computation", Vol. 1(1), pages 53-66, 1997.
[24]. A. Banerjee, S. Basu, C. Krumpelman, J. Ghosh, and R. Mooney. Model-based overlapping clustering. Proceedings of KDD2005, pages 100–106, 2005.
[25]. M. Deodhar, Consensus clustering of microarray data, 2006.