Abstract
A cross mutation-based differential evolution (CMDE) approach is proposed here to handle the complexity issue in clustering due to the data uncertainty, overlapping and rapid growth in size of data. In this CMDE, a novel mutation strategy and a centroid rearrangement scheme have been proposed for getting a better and consistent result. CMDE provides optimal cluster centres with minimum intra cluster distance and maximum accuracy percentage. A comparative analysis of the proposed approach with another five population based methods, such as dynamic shuffled differential evolution (DSDE), ant colony optimisation (ACO), artificial bee colony (ABC), particle swarm optimisation (PSO) and particle swarm optimisation with age-group topology (PSOAG) is carried out to justify the better clustering performance of the suggested method. These techniques are applied to seven real datasets and the results reveal the efficacy of the proposed algorithm for clustering in various fields.
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