Package: wnpp Severity: wishlist Owner: Edward Betts <edw...@4angle.com> X-Debbugs-Cc: debian-devel@lists.debian.org, debian-pyt...@lists.debian.org
* Package name : python-hdbscan Version : 0.8.33 Upstream Author : Leland McInnes <leland.mcin...@gmail.com> * URL : https://github.com/scikit-learn-contrib/hdbscan * License : BSD-3-clause Programming Lang: Python Description : Clustering based on density with variable density clusters HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a powerful clustering algorithm designed for discovering meaningful patterns in data. Unlike traditional clustering methods, HDBSCAN excels at identifying clusters of varying densities, making it particularly suitable for complex datasets where traditional approaches may struggle. . HDBSCAN operates by performing DBSCAN clustering over a range of epsilon values and then integrates these results to find a clustering that offers the best stability across the range. HDBSCAN is able to determine clusters with little or no parameter tuning. The primary parameter, minimum cluster size, is intuitive and straightforward to select, making it ideal for exploratory data analysis. . Key Features: - Robust to parameter selection: HDBSCAN returns meaningful clusters with minimal parameter tuning. - Support for varying densities: It can find clusters of varying densities, unlike DBSCAN. - High performance: HDBSCAN is significantly faster than many clustering algorithms, making it suitable for large datasets. - Comprehensive documentation: Tutorials and documentation are available on ReadTheDocs, making it easy to get started. I plan to maintain this package as part of the Python team.
