A Bidirectional Adaptive Bandwidth Mean Shift Strategy for Clustering

TL;DR

This paper introduces a bidirectional adaptive bandwidth mean shift strategy that enhances clustering by better escaping local maxima, combining advantages of existing adaptive bandwidth methods.

Contribution

It proposes a novel bidirectional adaptive bandwidth approach that improves mean shift clustering performance and robustness against local maxima.

Findings

Demonstrates improved clustering accuracy over existing methods

Shows better ability to escape local maxima

Validates effectiveness through experiments

Abstract

The bandwidth of a kernel function is a crucial parameter in the mean shift algorithm. This paper proposes a novel adaptive bandwidth strategy which contains three main contributions. (1) The differences among different adaptive bandwidth are analyzed. (2) A new mean shift vector based on bidirectional adaptive bandwidth is defined, which combines the advantages of different adaptive bandwidth strategies. (3) A bidirectional adaptive bandwidth mean shift (BAMS) strategy is proposed to improve the ability to escape from the local maximum density. Compared with contemporary adaptive bandwidth mean shift strategies, experiments demonstrate the effectiveness of the proposed strategy.