Abstract
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 approachis evaluated, in conjunction with three cluster validityindices, namely Davies-Bouldin index, Dunn’s index,and Calinski-Harabasz index. Experimental results are providedto illustrate the feasibility of the proposed approach.