Clustering probability distributions

被引：41

作者：

Tai Vo Van ^{[2
]}

Pham-Gia, T. ^{[1
]}

机构：

[1] Univ Moncton, Dept Stat, Moncton, NB E1A 3E9, Canada

[2] Can Tho Univ, Dept Math, Can Tho, Vietnam

来源：

JOURNAL OF APPLIED STATISTICS | 2010年 / 37卷 / 11期

基金：

加拿大自然科学与工程研究理事会;

关键词：

maximum function; cluster; L1-distance; Bayes error; hierarchical approach; EFFICIENT ALGORITHM;

D O I：

10.1080/02664760903186049

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This article presents some theoretical results on the maximum of several functions, and its use to define the joint distance of k probability densities, which, in turn, serves to derive new algorithms for clustering densities. Numerical examples are presented to illustrate the theory.

引用

页码：1891 / 1910

页数：20

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