A Divisive Hierarchical Clustering Algorithm Based on Soft Hyperspheric Partition
XIE Zhen-Ping, WANG Shi-Tong, WANG Xiao-Ming
School of Information Technology, Jiangnan University, Wuxi 214122 State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing 210016
Abstract Hierarchical clustering is a classical data clustering method, but with two disadvantages—computational complexity and sensitivity to noises and outliers. To avoid these problems, a new divisive hierarchical clustering method is presented, called soft hyperspheric partition based divisive hierarchical clustering (SHPDHC). A new partitioning strategy, soft hyperspheric partition (SHP), is introduced. This strategy is derived from the possibilistic clustering method. SHPDHC has low computational complexity and has the ability of weakening the influence of outliers existing in the dataset, meanwhile, SHPDHC can easily produce the natural number of clusters. The theoretical analysis and experimental results on artificial datasets and real images demonstrate the effectiveness of the proposed method.
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2008, Vol. 21 
