Lumbermark: Resistant Clustering by Chopping Up Mutual Reachability Minimum Spanning Trees

TL;DR

Lumbermark is a robust divisive clustering algorithm that effectively detects clusters of various sizes, densities, and shapes by chopping mutual reachability minimum spanning trees, reducing noise influence.

Contribution

It introduces a new clustering method that offers an alternative to HDBSCAN, with a fast implementation and the ability to produce user-specified cluster sizes.

Findings

Performs well on benchmark data

Handles clusters of varying sizes, densities, and shapes

Reduces influence of noise and outliers

Abstract

We introduce Lumbermark, a robust divisive clustering algorithm capable of detecting clusters of varying sizes, densities, and shapes. Lumbermark iteratively chops off large limbs connected by protruding segments of a dataset's mutual reachability minimum spanning tree. The use of mutual reachability distances smoothens the data distribution and decreases the influence of low-density objects, such as noise points between clusters or outliers at their peripheries. The algorithm can be viewed as an alternative to HDBSCAN that produces partitions with user-specified sizes. A fast, easy-to-use implementation of the new method is available in the open-source 'lumbermark' package for Python and R. We show that Lumbermark performs well on benchmark data and hope it will prove useful to data scientists and practitioners across different fields.