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. In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster size, is intuitive and easy to select. HDBSCAN is ideal for exploratory data analysis; it's a fast and robust algorithm that you can trust to return meaningful clusters (if there are any).

Features

  • The hdbscan package comes equipped with visualization tools
  • Understand your clustering results
  • Documentation available
  • Outlier Detection
  • Robust single linkage
  • Examples included

Project Samples

Project Activity

See All Activity >

Categories

Machine Learning

License

BSD License

Follow HDBSCAN

HDBSCAN Web Site

You Might Also Like
Gain insights and build data-powered applications Icon
Gain insights and build data-powered applications

Your unified business intelligence platform. Self-service. Governed. Embedded.

Chat with your business data with Looker. More than just a modern business intelligence platform, you can turn to Looker for self-service or governed BI, build your own custom applications with trusted metrics, or even bring Looker modeling to your existing BI environment.
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of HDBSCAN!

Additional Project Details

Operating Systems

Linux, Mac, Windows

Programming Language

Python

Related Categories

Python Machine Learning Software

Registered

2024-08-07