Automatic clustering by elitism-based multi-objective differential evolution

To arrange the uncategorised and unlabelled data into different clusters and finding the actual label of each datum from the huge volume by extracting useful and unique information is a real challenge. In this article, an automatic clustering by elitism-based multi-objective differential evolution (AC-EMODE) algorithm has been proposed to deal with partitioning the data into different clusters. This work includes three objectives to handle complex datasets. This generates a suitable Pareto front by simultaneously optimising three objectives. In addition to that, a very effective concept is followed for getting the best solution from the optimal Pareto front. A comparative analysis of the proposed approach with another six population-based methods has been carried out. These techniques are applied to ten datasets and the results reveal that the proposed approach can be considered as one of the alternative powerful methods for all data clustering applications in various fields.