{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/4"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"}],"enrichment":{"capability":"HDBSCAN performs hierarchical density-based clustering that automatically finds clusters of varying densities with minimal parameter tuning, and includes outlier detection and branch-detection capabilities.","skillfed_tags":["clustering","density-based","outlier-detection"],"use_cases":["Exploratory data analysis on unlabeled datasets where cluster count and density are unknown.","Outlier detection by accessing outlier_scores_ after fitting to identify anomalous points.","Clustering with automatic parameter selection when you want to avoid extensive tuning.","Soft clustering assignments to understand cluster membership confidence for each point.","Detecting branching or hierarchical structures in cluster data using BranchDetector."],"what_it_does":"HDBSCAN is a clustering algorithm that extends DBSCAN by performing density-based clustering over varying epsilon values and integrating results to find stable clusters. Unlike DBSCAN, it handles clusters of different densities and is robust to parameter selection\u2014the main tunable parameter, minimum cluster size, is intuitive to set. The package accepts arrays, dataframes, or sparse matrices of shape (num_samples x num_features), or distance matrices between samples, and depends on numpy, scipy, scikit-learn, and joblib.\n\nBeyond basic clustering, HDBSCAN provides outlier detection via the GLOSH algorithm, visualization tools for cluster hierarchies and reachability distances, soft clustering with membership strengths, and cluster persistence scores indicating stability. It also includes a RobustSingleLinkage implementation and a BranchDetector for detecting branching structures in clusters. The implementation prioritizes performance.","worth_installing":"Yes. HDBSCAN is actively maintained, has no known vulnerabilities, works with current Python versions (3.10\u20133.14), and solves a real problem\u2014automatic, parameter-light density-based clustering with variable cluster sizes. The medium install friction is manageable given prebuilt wheels. It is well-suited for exploratory clustering and outlier detection."},"id":"hdbscan","links":{"html":"https://skillfed.io/packages/hdbscan","md":"https://skillfed.io/packages/hdbscan.md","pypi":"https://pypi.org/project/hdbscan/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-01","license_spdx":null,"license_treatment":"permissive","name":"hdbscan","python_support":"supports_current","summary":"Clustering based on density with variable density clusters"},"popularity":{"monthly_downloads":2988759,"position":2798,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.8.44"}