HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection.

Based on the paper:

R. Campello, D. Moulavi, and J. Sander, Density-Based Clustering Based on Hierarchical Density Estimates In: Advances in Knowledge Discovery and Data Mining, Springer, pp 160-172. 2013

The hdbscan package inherits from sklearn classes, and thus drops in neatly next to other sklearn clusterers with an identical calling API. Similarly it supports input in a variety of formats: an array (or pandas dataframe, or sparse matrix) of shape (num_samples x num_features); an array (or sparse matrix) giving a distance matrix between samples.

import hdbscan

clusterer = hdbscan.HDBSCAN(min_cluster_size=10)
cluster_labels = clusterer.fit_predict(data)

Note that clustering larger datasets will require significant memory (as with any algorithm that needs all pairwise distances). Support for low memory/better scaling is planned but not yet implemented.

Fast install

pip install hdbscan

For a manual install get this package:

wget https://github.com/lmcinnes/hdbscan/archive/master.zip
unzip master.zip
rm master.zip
cd hdbscan-master

Install the requirements

sudo pip install -r requirements.txt

Install the package

python setup.py install

The hdbscan package is BSD licensed. Enjoy.