Computer Engineering & Science >
An Automatic Clustering Method Using SubSampling for the KDTree
Received date: 2010-02-26
Revised date: 2010-05-30
Online published: 2011-01-25
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 autoclustering method using the KDTree subsampling technique is proposed in this paper. The sample space is divided into subspaces using the KDTree method. In each subspace, the KDTree subsamples are produced by randomly sampling for later autoclustering. The KMeans method is used to optimize the cluster results of the subsamples. 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.
PAN Zhangming . An Automatic Clustering Method Using SubSampling for the KDTree[J]. Computer Engineering & Science, 2011 , 33(1) : 166 -170 . DOI: 10.3969/j.issn.1007130X.2011.
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