A Point Symmetry-Based Automatic Clustering Approach Using Differential Evolution

Clustering is a core problem in data mining and machine learning. It has innumerable applications in many fields. Recently, using the evolutionary algorithms for the clustering problem has become more and more popular. In this paper, we propose an automatic clustering differential evolution (DE) technique for the clustering problem. Our approach can be characterized by (i) proposing a modified point symmetry-based cluster validity index (CVI) as a measure of the validity of the corresponding partitioning, (ii) using the Kd-tree based nearest neighbor search to reduce the complexity of finding the closest symmetric point, and (iii) employing a new representation to represent an individual. Experiments have been conducted on 6 artificial data sets of diverse complexities. And the results indicate that our approach is suitable for both the symmetrical intra-clusters and the symmetrical inter-clusters. In addition, our approach is able to find the optimal number of clusters of the data. Furthermore, based on the comparison with the original point symmetry-based CVI, our proposed point symmetry-based CVI shows better performance in terms of the F-measure and the number of clusters found.