Clustering based on density with variable density clusters
Project description
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, and explaining how HDBSCAN works 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)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.
Installing
Fast install
pip install hdbscanFor a manual install get this package:
wget https://github.com/lmcinnes/hdbscan/archive/master.zip
unzip master.zip
rm master.zip
cd hdbscan-masterInstall the requirements
sudo pip install -r requirements.txtInstall the package
python setup.py installLicensing
The hdbscan package is BSD licensed. Enjoy.
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