pdfClustering function

Probability Density Distribution Clustering

Probability Density Distribution Clustering

Clustering via non parametric density estimation

pdfClustering(Data, PlotIt = FALSE, ...)

Arguments

  • Data: [1:n,1:d] matrix of dataset to be clustered. It consists of n cases of d-dimensional data points. Every case has d attributes, variables or features.
  • PlotIt: Default: FALSE, if TRUE plots the first three dimensions of the dataset with colored three-dimensional data points defined by the clustering stored in Cls
  • ...: Further arguments to be set for the clustering algorithm, if not set, default arguments are used.

Details

Cluster analysis is performed by the density-based procedures described in Azzalini and Torelli (2007) and Menardi and Azzalini (2014), and summarized in Azzalini and Menardi (2014).

Returns

List of - Cls: [1:n] numerical vector with n numbers defining the classification as the main output of the clustering algorithm. It has k unique numbers representing the arbitrary labels of the clustering.

  • Object: Object defined by clustering algorithm as the other output of this algorithm

References

Azzalini, A., Menardi, G. (2014). Clustering via nonparametric density estimation: the R package pdfCluster. Journal of Statistical Software, 57(11), 1-26, URL http://www.jstatsoft.org/v57/i11/.

Azzalini A., Torelli N. (2007). Clustering via nonparametric density estimation. Statistics and Computing. 17, 71-80.

Menardi, G., Azzalini, A. (2014). An advancement in clustering via nonparametric density estimation. Statistics and Computing. DOI: 10.1007/s11222-013-9400-x.

Author(s)

Michael Thrun

Examples

data('Hepta') out=pdfClustering(Hepta$Data,PlotIt=FALSE)
  • Maintainer: Michael Thrun
  • License: GPL-3
  • Last published: 2023-10-19