Finding the optimal number of clusters using genetic algorithms
Yongguo Liu, Mao Ye, Jun Peng, Hong Qi Wu · 2008
In clustering analysis, many methods require the designer to provide the number of clusters. Unfortunately, the designer has no idea, in general, about this information beforehand. In this paper, we propose a genetic algorithm based clustering method called Automatic Genetic Clustering for Unknown K (AGCUK). The AGCUK algorithm is able to automatically provide the number of clusters and find the clustering partition. The Davies-Bouldin index is employed to measure the validity of the clusters. Experimental results on artificial and real-life data sets are given to illustrate the effectiveness of the AGCUK algorithm.