rfClustering

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.

robust

cluster

tree

A suite of machine learning algorithms written in C++ with the R
interface contains several learning techniques for classification and regression.
Predictive models include e.g., classification and regression trees with
optional constructive induction and models in the leaves, random forests, kNN,
naive Bayes, and locally weighted regression. All predictions obtained with these
models can be explained and visualized with the 'ExplainPrediction' package.
This package is especially strong in feature evaluation where it contains several variants of
Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini,
information gain, MDL, and DKM. These methods can be used for feature selection
or discretization of numeric attributes.
The OrdEval algorithm and its visualization is used for evaluation
of data sets with ordinal features and class, enabling analysis according to the
Kano model of customer satisfaction.
Several algorithms support parallel multithreaded execution via OpenMP.
The top-level documentation is reachable through ?CORElearn.

Marko Robnik-Sikonja

CORElearn

Classification, Regression and Feature Evaluation

rfClustering function

<dl> <dt>model</dt>
<dd>a random forest model returned by <code>CoreModel</code></dd> <dt>noClusters</dt>
<dd>number of clusters</dd></dl>

Arguments

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

Author

Random forest based clustering — rfClustering

<dl>

 <dt>model</dt>
<dd>a random forest model returned by <code>CoreModel</code></dd>

 <dt>noClusters</dt>
<dd>number of clusters</dd>

</dl>

rfClustering: Random forest based clustering

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples