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pdfCluster

Cluster Analysis via Nonparametric Density Estimation

Cluster analysis via nonparametric density estimation is performed. Operationally, the kernel method is used throughout to estimate the density. Diagnostics methods for evaluating the quality of the clustering are available. The package includes also a routine to estimate the probability density function obtained by the kernel method, given a set of data with arbitrary dimensions.

Functions (20)

adj.rand.index

Adjusted Rand index

dbs

Density-based silhouette information methods

Class "dbs"

Extracts groups

Normal optimal choice of smoothing parameter in density estimation

Sample smoothing parameters in adaptive density estimation

Kernel estimate of a probability density function.

kepdf-class

Class "kepdf"

Olive oil data

pdfClassification

Classification of low density data

Clustering via nonparametric density estimation

pdfCluster-class

Class "pdfCluster"

pdfCluster-package

The pdfCluster package: summary information

plot-methods

Methods for function plot

Plot objects of class dbs

Plot objects of class kepdf

plot.pdfCluster

Plot objects of class pdfCluster

show-methods

Methods for Function show

summary-methods

Methods for Function summary

wine

Wine data

pdfCluster

Cluster Analysis via Nonparametric Density Estimation

v1.0-3

GPL-2

Authors

Adelchi Azzalini, Giovanna Menardi for the current version; Adelchi Azzalini, Giovanna Menardi and Tiziana Rosolin up to version 0.1-13.

Initial release

2018-12-04

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