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Unsupervised Random Forest (Random Forest Clustering)

Project description

URF (Unsupervised Random Forest, or Random Forest Clustering) is a python implementation of the paper: Shi, T., & Horvath, S. (2006). Unsupervised learning with random forest predictors. Journal of Computational and Graphical Statistics, 15(1), 118-138.


conda install -c bioconda pycluster


tar -zxvf Pycluster-1.54.tar.gz
cd Pycluster-1.54
python install


pip install URF


from sklearn.datasets import load_iris
from URF.main import random_forest_cluster, plot_cluster_result
iris = load_iris()
X =
y =

clf, prox_mat, cluster_ids = random_forest_cluster(X, k=3, max_depth=20, random_state=0)
plot_cluster_result(prox_mat, cluster_ids, marker=y)

If you encountered an error like

> QXcbConnection: Could not connect to display

then you need to add these codes to the very beginning of your file:

import matplotlib as mpl

and you must assign the output file when you call plot_cluster_result, like this:

plot_cluster_result(prox_mat, cluster_ids, marker=y, output="test_123.png")

Project details

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Files for URF, version 0.0.5
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