hdbscan 0.4.2

Clustering based on density with variable density clusters

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

Notebooks comparing HDBSCAN to other clustering algorithms, explaining how HDBSCAN works and comparing performance with other python clustering implementations are available.

How to use HDBSCAN

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)

Performance

Significant effort has been put into making the hdbscan implementation as fast as possible. It is more than twice as fast as the reference implementation in Java and is currently faster highly optimized single linkage implementations in C and C++. version 0.3 performance can be seen in this notebook . Version 0.4 brings a new minimum spanning tree algorithm and dramatic speedups, particularly for low dimensional data.

Additional functionality

The hdbscan package comes equipped with visualization tools to help you understand your clustering results. After fitting data the clusterer object has attributes for:

• The condensed cluster hierarchy

• The robust single linkage cluster hierarchy

• The reachability distance minimal spanning tree

All of which come equipped with methods for plotting and converting to Pandas or NetworkX for further analysis. See the notebook on how HDBSCAN works for examples and further details.

The clusterer objects also have an attribute providing cluster membership strengths, resulting in optional soft clustering (and no further compute expense)

Robust single linkage

The hdbscan package also provides support for the robust single linkage clustering algorithm of Chaudhuri and Dasgupta. As with the HDBSCAN implementation this is a high performance version of the algorithm outperforming scipy’s standard single linkage implementation. The robust single linkage hierarchy is available as an attribute of the robust single linkage clusterer, again with the ability to plot or export the hierarchy, and to extract flat clusterings at a given cut level and gamma value.

Example usage:

import hdbscan

clusterer = hdbscan.RobustSingleLinkage(cut=0.125, k=7)
cluster_labels = clusterer.fit_predict(data)
hierarchy = clusterer.cluster_hierarchy_
alt_labels = hierarchy.get_clusters(0.100, 5)
hierarchy.plot()

Based on the paper:

K. Chaudhuri and S. Dasgupta. “Rates of convergence for the cluster tree.” In Advances in Neural Information Processing Systems, 2010.

Installing

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

Licensing

The hdbscan package is BSD licensed. Enjoy.