A dynamic genetic clustering algorithm for automatic choice of the number of clusters
Hong He, Yonghong Tan · 2011
One of the most difficult problems in cluster analysis is how many clusters are appropriate for the description of a given system. In this paper, a novel dynamic genetic clustering algorithm (DGCA) is proposed to automatically search for the best number of clusters and the corresponding partitions. In the DGCA, a maximum attribute range partition approach is used in the population initialization in order to overcome the sensitivity of clustering algorithms to initial partitions. Furthermore, the methods of two-step selection and mutation operations are developed to exploit the search capability of the algorithm. Finally, the comparison among the DGCA, k-means algorithm and the standard genetic k-means clustering algorithm (SGKC) is illustrated with several artificial and real life data sets.