asymmetric3

<p>Each cluster has been generated according to a multivariate asymmetric Gaussian distribution, with shape 3, covariance matrix equal to the identity matrix and randomly generated centres.
</p>

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 centre is a constant. PD-clustering is a flexible method that can be used with non-spherical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different size. GPDC and TPDC uses 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

asymmetric3 function

<p>A data frame with 800 observations on 101 variables.
 The first variable is the membership labels.</p>

asymmetric3: Asymmetric data set shape 3

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