GMVECluster | R Documentation |
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)
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 |
Implementation of the GMVE clustering algorithm as proposed by Jolion et al. (1991).
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 |
Jose G. Tamez-Pena
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.
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