scikit-learn-contrib/hdbscan

Health v2 · maintenance only

Adoption not part of the score

3136 stars · 534 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A high-performance Python implementation of the HDBSCAN hierarchical density-based clustering algorithm, part of the scikit-learn-contrib ecosystem. It finds clusters of varying densities with minimal parameter tuning, making it well suited to exploratory data analysis.

Use cases

• cluster data with varying densities without tuning epsilon
• find meaningful clusters in exploratory data analysis
• replace DBSCAN with a more robust density-based clustering algorithm
• cluster high-dimensional embeddings like sentence or image vectors
• identify noise and outliers in unlabeled datasets
• perform hierarchical density-based clustering in Python

When to choose

• you need clustering that works well with little or no parameter tuning
• your data contains clusters of varying densities that DBSCAN handles poorly
• you want a scikit-learn-compatible, well-documented clustering library

When to avoid

• you need to specify an exact number of clusters, as HDBSCAN determines them automatically
• you need extremely fast clustering on very large datasets where simpler algorithms like k-means suffice
• you require a non-Python environment

Facets

library · maturity stable

machine-learning data-science machine-learning data-science analytics python cross-platform clustering hdbscan dbscan density-based-clustering unsupervised-learning scikit-learn

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