An Ant Colony Optimization based Evolutionary Multi-objective Clustering for Overlapping Clusters Detection (ACOEMCOC) - Punithavathy, S (International Journal of Computer Sciences and Engineering, 2025)
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