Clustering via Nonparametric Density Estimation: The R Package pdfCluster

被引：0

作者：

Azzalini, Adelchi ^{[1
]}

Menardi, Giovanna ^{[1
]}

机构：

[1] Univ Padua, Dipartimento Sci Stat, I-35100 Padua, Italy

来源：

JOURNAL OF STATISTICAL SOFTWARE | 2014年 / 57卷 / 11期

关键词：

cluster analysis; graph; kernel methods; nonparametric density estimation; R; unsupervised classification; Delaunay triangulation; TREE;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The R package pdfCluster performs cluster analysis based on a nonparametric estimate of the density of the observed variables. Functions are provided to encompass the whole process of clustering, from kernel density estimation, to clustering itself and subsequent graphical diagnostics. After summarizing the main aspects of the methodology, we describe the features and the usage of the package, and finally illustrate its application with the aid of two data sets.

引用

页数：26

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