ClusteringRandomForest

Signals

Inputs:

• Data

  Data to be used for learning.

Outputs:

• Learner or Classifier

Description

A clustering random forest is a random forest consisting of clustering trees. The usage is straightforward and the setting are described below.

Settings

• Number of trees in forest

  Number of trees in forest.

• Stop splitting nodes at depth

  Maximal depth of tree.

• Minimal majority class proportion

  Minimal proportion of the majority class value each of the class variables has to reach to stop induction (only used for classification).

• Min mean squared error

  Minimal mean squared error each of the class variables has to reach to stop induction (only used for regression).

• Min. instances in leaves

  Minimal number of instances in leaves. Instance count is weighed.

• Feature scorer

  • Inter dist (default) - Euclidean distance between centroids of clusters
  • Intra dist - average Euclidean distance of each member of a cluster to the centroid of that cluster
  • Silhouette - silhouette (http://en.wikipedia.org/wiki/Silhouette_(clustering)) measure calculated with euclidean distances between clusters instead of elements of a cluster.
  • Gini-index - calculates the Gini-gain index, should be used with class variables with nominal values