Local Cluster Cardinality Estimation for Adaptive Mean Shift

TL;DR

This paper introduces an adaptive mean shift algorithm that estimates local cluster cardinality using distance distributions, enabling dynamic bandwidth adjustment and improved clustering performance on datasets with varying local scales.

Contribution

The novel method estimates local cluster cardinality from distance distributions to adaptively tune mean shift parameters, outperforming existing adaptive mean shift algorithms.

Findings

Outperforms recent adaptive mean shift methods on original datasets

Demonstrates competitive results on broader clustering benchmarks

Effectively adapts to datasets with varying local scales

Abstract

This article presents an adaptive mean shift algorithm designed for datasets with varying local scale and cluster cardinality. Local distance distributions, from a point to all others, are used to estimate the cardinality of the local cluster by identifying a local minimum in the density of the distance distribution. Based on these cardinality estimates, local cluster parameters are then computed for the entire cluster in contrast to KDE-based methods, which provide insight only into localized regions of the cluster. During the mean shift execution, the cluster cardinality estimate is used to adaptively adjust the bandwidth and the mean shift kernel radius threshold. Our algorithm outperformed a recently proposed adaptive mean shift method on its original dataset and demonstrated competitive performance on a broader clustering benchmark.