# Accelerated Hierarchical Density Clustering

## TL;DR

This paper introduces an accelerated hierarchical density clustering algorithm that matches DBSCAN's speed, supports variable density clusters, and removes the need for complex parameter tuning, making it a preferred method.

## Contribution

The paper presents an improved version of HDBSCAN* that is faster, easier to use, and capable of handling variable density clusters, advancing density-based clustering techniques.

## Key findings

- Comparable performance to DBSCAN
- Supports variable density clusters
- Eliminates need for distance scale parameter

## Abstract

We present an accelerated algorithm for hierarchical density based clustering. Our new algorithm improves upon HDBSCAN*, which itself provided a significant qualitative improvement over the popular DBSCAN algorithm. The accelerated HDBSCAN* algorithm provides comparable performance to DBSCAN, while supporting variable density clusters, and eliminating the need for the difficult to tune distance scale parameter. This makes accelerated HDBSCAN* the default choice for density based clustering. Library available at: https://github.com/scikit-learn-contrib/hdbscan