Unsupervised Clustering by Means of Hierarchical Differential Evolution Algorithm

In solving the hard clustering problem, the number of clusters in general is unknown for most real-world applications. Therefore, clustering becomes a trial-and-error task and the clustering result is often not very promising especially when the number of clusters is difficult to guess. In this paper, we propose an unsupervised clustering approach which utilizes a hierarchical differential evolution algorithm. The proposed approach can effectively search the proper number of clusters and simultaneously determine the cluster centers. The performance of the proposed approach is evaluated, in conjunction with three cluster validity indices, namely Davies-Bouldin index, Dunnpsilas index, and Calinski-Harabasz index. Experimental results are provided to illustrate the feasibility of the proposed approach.