Abstract:

The evolution theory based automatic clustering method has advantages in finding the global optimum and the cluster number, but shows the lack of efficiency in machine time. An autoclustering method using the KDTree subsampling technique is proposed in this paper. The sample space is divided into subspaces using the KDTree method. In each subspace, the KDTree subsamples are produced by randomly sampling for later autoclustering. The KMeans method is used to optimize the cluster results of the subsamples. The method can effectively overcome the defect of biased distribution for random subsamples and give good cluster results even for small samples. The simulation results show that the method remarkably reduces the machine time for auto clustering without decreasing the clustering effect.

Key words: KDtree;subsample;differential evolution;automatic clustering