Adaptive Affinity Propagation Clustering

TL;DR

This paper introduces an adaptive affinity propagation clustering method that automatically tunes parameters to improve clustering quality and stability, addressing key limitations of the original AP algorithm.

Contribution

It proposes adaptive techniques for preference scanning and damping adjustment, enhancing AP's ability to find optimal clusters and eliminate oscillations.

Findings

Adaptive AP outperforms standard AP in clustering quality.

The method effectively eliminates oscillations during clustering.

Experimental results validate the approach on simulated and real datasets.

Abstract

Affinity propagation clustering (AP) has two limitations: it is hard to know what value of parameter 'preference' can yield an optimal clustering solution, and oscillations cannot be eliminated automatically if occur. The adaptive AP method is proposed to overcome these limitations, including adaptive scanning of preferences to search space of the number of clusters for finding the optimal clustering solution, adaptive adjustment of damping factors to eliminate oscillations, and adaptive escaping from oscillations when the damping adjustment technique fails. Experimental results on simulated and real data sets show that the adaptive AP is effective and can outperform AP in quality of clustering results.