Agglomerative hierarchical clustering of continuous variables based on mutual information

被引：37
|
作者：
Kojadinovic, I [1 ]
机构：
[1] Univ La Reunion, IREMIA, 15 Ave Rene Cassin BP 7151, F-97715 St Denis Messageries 9, Reunion, France
来源：
COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2004年 / 46卷 / 02期
关键词：
agglomerative hierarchical clustering; continuous variables; mutual information; Shannon entropy; redundancy; adaptive kernel density estimation;
D O I：
10.1016/S0167-9473(03)00153-1
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In order to study interdependencies among continuous variables in the framework of a data analysis problem, an agglomerative hierarchical clustering of the set of variables is performed. The similarity measure used within the clustering algorithm is based on the notion of mutual information. Recent results on the estimation of this measure of stochastic dependence are presented and the behavior of the clustering algorithm is studied on several artificial problems, i.e., which "structure" is known. (C) 2003 Elsevier B.V. All rights reserved.
引用
收藏
页码：269 / 294
页数：26
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