The Function will return the set of Gaussian Ellipsoids that best model the data
GMVECluster(dataset,
p.threshold=0.975,
samples=10000,
p.samplingthreshold=0.50,
sampling.rate = 3,
jitter=TRUE,
tryouts=25,
pca=TRUE,
verbose=FALSE)The data set to be clustered
The p-value threshold of point acceptance into a set.
If the set is large, The number of random samples
Defines the maximum distance between set candidate points
Uniform sampling rate for candidate clusters
If true, will jitter the data set
The number of cluster candidates that will be analyed per sampled point
If TRUE, it will use the PCA transform for dimension reduction
If true it will print the clustering evolution
The numeric vector with the cluster label of each point
The numeric vector with the cluster label of each point
The list of cluster centers
The list of cluster covariance
The list of robust covariances per cluster
The number of discovered clusters
The characer vector with the names of the features used
The jittered dataset
Implementation of the GMVE clustering algorithm as proposed by Jolion et al. (1991).
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.