Automatic clustering using genetic algorithms

In face of the clustering problem, many clustering methods usually require the designer to provide the number of clusters as input. Unfortunately, the designer has no idea, in general, about this information beforehand. In this article, we develop a genetic algorithm based clustering method called automatic genetic clustering for unknown K (AGCUK). In the AGCUK algorithm, noising selection and division-absorption mutation are designed to keep a balance between selection pressure and population diversity. In addition, the Davies-Bouldin index is employed to measure the validity of clusters. Experimental results on artificial and real-life data sets are given to illustrate the effectiveness of the AGCUK algorithm in automatically evolving the number of clusters and providing the clustering partition.