Distribution free decomposition of multivariate data

被引：106

作者：

Comaniciu, D ^{[1
]}

Meer, P ^{[1
]}

机构：

[1] Rutgers State Univ, Dept Elect & Comp Engn, Piscataway, NJ 08855 USA

来源：

PATTERN ANALYSIS AND APPLICATIONS | 1999年 / 2卷 / 01期

关键词：

convergence; gradient density estimation; mean shift procedure; mode seeking; nonparametric cluster analysis; range searching;

D O I：

10.1007/s100440050011

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a practical approach to nonparametric cluster analysis of large data sets. The number of clusters and the cluster centres are automatically derived by mode seeking with the mean shift procedure on a reduced see of points randomly selected from the data. The cluster boundaries are delineated using a k-nearest neighbour technique. The proposed algorithm is stable and efficient, a 10,000 point data set being decomposed in only a few seconds. Complex clustering examples and applications are discussed, and convergence of the gradient ascent mean shift procedure is demonstrated for arbitrary distribution and cardinality of the data.

引用

页码：22 / 30

页数：9

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