Locally Adaptive Hierarchical Cluster Termination With Application To Individual Tree Delineation

TL;DR

This paper introduces a locally adaptive termination method for hierarchical clustering that adapts to the data's structure, providing a multi-scale alternative to traditional threshold-based methods.

Contribution

It proposes a novel locally adaptive termination procedure for agglomerative hierarchical clustering, improving multi-scale analysis capabilities.

Findings

Effective in multi-scale clustering scenarios

Outperforms traditional threshold-based methods

Applicable to various hierarchical clustering tasks

Abstract

A clustering termination procedure which is locally adaptive (with respect to the hierarchical tree of sets representative of the agglomerative merging) is proposed, for agglomerative hierarchical clustering on a set equipped with a distance function. It represents a multi-scale alternative to conventional scale dependent threshold based termination criteria.