outliers

<p>Each cluster has been generated according to a multivariate Gaussian distribution, with centers c randomly generated. For each cluster, 20% of uniform distributed outliers have been generated at a distance included in max(x-c) and max(x-c)+5 form the center.
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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

outliers function

<p>A data frame with 960 observations on the following 101 variables.
  The first variable corresponds to the membership</p>

outliers: Data set with outliers

Description

Usage

Arguments

Format

Examples