Random forest based clustering
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.
rfClustering(model, noClusters=4)
model |
a random forest model returned by |
noClusters |
number of clusters |
The method calls pam function for clustering, initializing its distance matrix with random forest based similarity by calling
rfProximity with argument model.
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.
John Adeyanju Alao (as a part of his BSc thesis) and Marko Robnik-Sikonja (thesis supervisor)
Leo Breiman: Random Forests. Machine Learning Journal, 45:5-32, 2001
set<-iris md<-CoreModel(Species ~ ., set, model="rf", rfNoTrees=30, maxThreads=1) mdCluster<-rfClustering(md, 5) destroyModels(md) # clean up
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