rfClustering: Random forest based clustering

In CORElearn: Classification, Regression and Feature Evaluation

Random forest based clustering

Description

Creates a clustering of random forest training instances. Random forest provides proximity of its training instances based on their out-of-bag classification. This information is usually passed to visualizations (e.g., scaling) and attribute importance measures.

Usage

rfClustering(model, noClusters=4)

Arguments

model	a random forest model returned by CoreModel
noClusters	number of clusters

Details

The method calls pam function for clustering, initializing its distance matrix with random forest based similarity by calling rfProximity with argument model.

Value

An object of class pam representing the clustering (see ?pam.object for details), the most important being a vector of cluster assignments (named cluster) to training instances used to generate the model.

Author(s)

John Adeyanju Alao (as a part of his BSc thesis) and Marko Robnik-Sikonja (thesis supervisor)

References

Leo Breiman: Random Forests. Machine Learning Journal, 45:5-32, 2001

See Also

CoreModel rfProximity pam

Examples

set<-iris
md<-CoreModel(Species ~ ., set, model="rf", rfNoTrees=30, maxThreads=1)
mdCluster<-rfClustering(md, 5)

destroyModels(md) # clean up

CORElearn documentation built on Nov. 18, 2022, 5:08 p.m.