Automatic Hard Clustering Using Improved Differential Evolution Algorithm

This chapter describes a Differential Evolution (DE) based algorithm for the automatic clustering of large unlabeled datasets. In contrast to most of the existing clustering techniques, the proposed algorithm requires no prior knowledge of the data to be classified. Rather, it determines the optimal number of clusters in the data ‘on the run’. Superiority of the new method has been demonstrated by comparing it with two recently developed partitional clustering techniques and one popular hierarchical clustering algorithm. The partitional clustering algorithms are based on Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm. An interesting practical application of the proposed method to automatic segmentation of images is also illustrated.

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