A Clustering Algorithm Based on the Trace of Sample Covariance Matrix
HUANG XiaoBin1,3, WANG JianWei1, ZHANG Yan2
1.College of Electronics Science and Technology, National University of Defense Technology, Changsha 410073 2.College of Computer, National University of Defense Technology, Changsha 410073 3.Department of Information Engineering, Air Force Radar Academy, Wuhan 430010
Abstract：Aiming at the shortage of traditional clustering algorithm when dealing data with some nonsphericalshape distribution, a novel clustering algorithm based on the trace of sample covariance matrix is presented in this paper. This algorithm is made up of the three main parts-uniform for data, constitution of initial patterns and fusion of initial patterns. The simulation results show that compared with the traditional FCA, the proposed algorithm has good clustering performance for data with some nonsphericalshape distribution without the number of clustering.
黄晓斌，万建伟，张燕. 基于样本协方差矩阵迹的聚类算法[J]. 模式识别与人工智能, 2006, 19(1): 79-83.
HUANG XiaoBin, WANG JianWei, ZHANG Yan. A Clustering Algorithm Based on the Trace of Sample Covariance Matrix. , 2006, 19(1): 79-83.
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2006, Vol. 19 
