摘要
层次聚类是一种常用的聚类方法,但传统的层次聚类面临着计算复杂度较大、抗噪音和例外点干扰能力较弱的问题.本文以可能性聚类方法为基础,首先提出软边界球分算法,可实现对数据集合理分裂.随后将这一策略与分裂式层次聚类过程相结合,构造一种基于软边界球分的分裂式层次聚类算法(SHPDHC).SHPDHC 具有较低的计算复杂度.与此同时,它能较好地发现自然数据类,确定出合理的聚类数目,并能自适应划分出例外数据点.理论分析与对人工数据集的聚类实验结果证明了上述几点.最后我们将 SHPDHC 应用于一类阴影图像的分割中,同样取得良好效果.
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.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2008年第4期559-568,共10页
Pattern Recognition and Artificial Intelligence
基金
2004年教育部优秀人才支持计划项目(No.NCET-04-0496)
教育部重点科学研究项目(No.105087)资助
关键词
软边界球分(SHP)
分裂式层次聚类
图像分割
例外类
Soft Hyperspheric Partition （SHP）, Divisive Hierarchical Chastering, ImageSegmentation, Outliers Class
