GMVECluster {FRESA.CAD}

Set Clustering using the Generalized Minimum Volume Ellipsoid (GMVE)

Description

The Function will return the set of Gaussian Ellipsoids that best model the data

Usage

	GMVECluster(dataset, 
	            p.threshold=0.975,
	            samples=10000,
	            p.samplingthreshold=0.50,
	            sampling.rate = 3,
	            jitter=TRUE,
	            tryouts=25,
	            pca=TRUE,
	            verbose=FALSE)

Arguments

dataset

The data set to be clustered

p.threshold

The p-value threshold of point acceptance into a set.

samples

If the set is large, The number of random samples

p.samplingthreshold

Defines the maximum distance between set candidate points

sampling.rate

Uniform sampling rate for candidate clusters

jitter

If true, will jitter the data set

tryouts

The number of cluster candidates that will be analyed per sampled point

pca

If TRUE, it will use the PCA transform for dimension reduction

verbose

If true it will print the clustering evolution

Details

Implementation of the GMVE clustering algorithm as proposed by Jolion et al. (1991).

Value

cluster

The numeric vector with the cluster label of each point

classification

The numeric vector with the cluster label of each point

centers

The list of cluster centers

covariances

The list of cluster covariance

robCov

The list of robust covariances per cluster

k

The number of discovered clusters

features

The characer vector with the names of the features used

jitteredData

The jittered dataset

Author(s)

Jose G. Tamez-Pena

References

Jolion, Jean-Michel, Peter Meer, and Samira Bataouche. "Robust clustering with applications in computer vision." IEEE Transactions on Pattern Analysis & Machine Intelligence 8 (1991): 791-802.