Fast Multicore HDBSCAN

The fast_hdbscan library provides a simple implementation of the HDBSCAN clustering algorithm designed specifically for high performance on multicore machine with low dimensional data. The algorithm runs in parallel and can make effective use of as many cores as you wish to throw at a problem. It is thus ideal for large SMP systems, and even modern multicore laptops.

This library provides a re-implementation of a subset of the HDBSCAN algorithm that is compatible with the hdbscan library for data that is Euclidean and low dimensional. The primary advantages of this library over the standard hdbscan library are:

  • this library can easily use all available cores to speed up computation;

  • this library has much faster implementations of tree condensing and cluster extraction;

  • this library is much simpler and more approachable for extending or using components from;

  • this library is built on numba and has less issues with binaries and compilation.

This library does not support all the features and input formats available in the hdbscan library.

Basic Usage

The fast_hdbscan library follows the hdbscan library in using the sklearn API. You can use the fast_hdbscan class HDBSCAN exactly as you wuld that of the hdbscan library with the caveat that fast_hdbscan only supports a subset of the parameters and options of hdbscan. Nonetheless, if you have low-dimensional Euclidean data (e.g. the output of UMAP), you can use this library as a straightforward drop in replacement for hdbscan:

import fast_hdbscan
from sklearn.datasets import make_blobs

data, _ = make_blobs(1000)

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


fast_hdbscan requires:

  • numba

  • numpy

  • scikit-learn

fast_hdbscan can be installed via pip:

pip install fast_hdbscan

User Guide


The algorithm used here is an adaptation of the algorithms described in the papers:

McInnes L, Healy J. Accelerated Hierarchical Density Based Clustering In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, pp 33-42. 2017 [pdf]

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

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