智能系统学报2015，Vol.10Issue(5)：712-721,10.DOI:10.11992/tis.201410028
基于密度的统计合并聚类算法
Density-based statistical merging clustering algorithm
摘要
Abstract
The ability of existing clustering algorithms to deal with noise is poor, and the speed is slow, instead this paper proposes a density-based statistical merging clustering algorithm ( DSMC ) . The new algorithm takes each group of data points as a set of independent random variables, and gathers statistical criteria from the independent bounded difference inequality. Meanwhile, combined with the density information of the data points, the DSMC al-gorithm takes the descending order of the density as the merging order in the process of condensation, and thereby achieves statistical merging of different types of data points. The experimental results with both artificial datasets and real datasets show that the DSMC algorithm can not only deal with convex data set, and also has good clustering effects on nonconvex shaped, overlapped and noisy, data sets. This proves that the algorithm has good applicability and validity.关键词
数据点/密度/随机变量/合并/聚类/噪声Key words
data points/density/random variable/merging/clustering algorithm/noise分类
数理科学引用本文复制引用
刘贝贝,马儒宁,丁军娣..基于密度的统计合并聚类算法[J].智能系统学报,2015,10(5):712-721,10.基金项目
国家自然科学基金资助项目(61103058). （61103058）
