print.FPDclustering

Lists the available components for the given object

Probabilistic distance clustering (PD-clustering) is an iterative, distribution-free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership under the constraint that the product of the probability and the distance of each point to any cluster center is a constant. PD-clustering is a flexible method that can be used with elliptical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different sizes. GPDC and TPDC use a dissimilarity measure based on densities. Factor PD-clustering (FPDC) is a factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high-dimensional data sets.

Cristina Tortora

FPDclustering

PD-Clustering and Related Methods

Noe Vidales

Francesco Palumbo

Tina Kalra

print.FPDclustering function

<dl> <dt>x</dt>
<dd>an object of class FPDclustering</dd><dt>...</dt>
<dd>Additional parameters for the function ls</dd>
</dl>

Arguments

Author

Print for FPDclustering objects — print.FPDclustering

<dl>

 <dt>x</dt>
<dd>an object of class FPDclustering</dd>

<dt>...</dt>
<dd>Additional parameters for the function ls</dd>


</dl>

print.FPDclustering: Print for FPDclustering objects

Description

Usage

Arguments

Author