Returns the set of Gaussian Ellipsoids that best model the data
clusterISODATA(dataset,
clusteringMethod=GMVECluster,
trainFraction=0.99,
randomTests=10,
jaccardThreshold=0.45,
isoDataThreshold=0.75,
plot=TRUE,
...)The data set to be clustered
The clustering method.
The fraction of the data used to train the clusters
The number of clustering sets that will be generated
The minimum Jaccard index to be considered for data clustering
The minimum distance (as p.value) between gaussian clusters
If true it will plot the clustered points
Parameter list to be passed to the clustering method
The numeric vector with the cluster label of each point
The numeric vector with the cluster label of each point
The list of robust covariances per cluster
The mean of jaccard index per data point
The list of cluster centers
The list of cluster covariance
The characer vector with the names of the features used
The data will be clustered N times as defined by a number of randomTests. After clustering, the Jaccard Index map will be generated and ordered from high to low. The mean clusters parameters (Covariance sets) associated with the point with the highest Jaccard index will define the first cluster. A cluster will be added if the Mahalanobis distance between clusters is greater than the given acceptance p.value (isoDataThreshold) Only clusters associated with points with a Jaccard index greater than jaccardThreshold will be considered.