clusterISODATA {FRESA.CAD}

Cluster Clustering using the Isodata Approach

Description

Returns the set of Gaussian Ellipsoids that best model the data

Usage

   clusterISODATA(dataset,
                 clusteringMethod=GMVECluster,
                 trainFraction=0.99,
                 randomTests=10,
                 jaccardThreshold=0.45,
                 isoDataThreshold=0.75,
                 plot=TRUE,
                 ...)

Arguments

dataset

The data set to be clustered

clusteringMethod

The clustering method.

trainFraction

The fraction of the data used to train the clusters

randomTests

The number of clustering sets that will be generated

jaccardThreshold

The minimum Jaccard index to be considered for data clustering

isoDataThreshold

The minimum distance (as p.value) between gaussian clusters

plot

If true it will plot the clustered points

...

Parameter list to be passed to the clustering method

Details

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.

Value

cluster

The numeric vector with the cluster label of each point

classification

The numeric vector with the cluster label of each point

robustCovariance

The list of robust covariances per cluster

pointjaccard

The mean of jaccard index per data point

centers

The list of cluster centers

covariances

The list of cluster covariance

features

The characer vector with the names of the features used

Author(s)

Jose G. Tamez-Pena