rfClustering {CORElearn} | R Documentation |
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 |
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
Examples
set<-iris
md<-CoreModel(Species ~ ., set, model="rf", rfNoTrees=30, maxThreads=1)
mdCluster<-rfClustering(md, 5)
destroyModels(md) # clean up