Cluster Analysis via Nonparametric Density Estimation
Adjusted Rand index
Class "dbs"
Density-based silhouette information methods
Extracts groups
Normal optimal choice of smoothing parameter in density estimation
Sample smoothing parameters in adaptive density estimation
Class "kepdf"
Kernel estimate of a probability density function.
Classification of low density data
Class "pdfCluster"
The pdfCluster package: summary information
Clustering via nonparametric density estimation
Methods for function plot
Plot objects of class dbs
Plot objects of class kepdf
Plot objects of class pdfCluster
Methods for Function show
Methods for Function summary
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.