Geometric Affinity Propagation for Clustering with Network Knowledge

TL;DR

This paper introduces geometric-AP, an extension of affinity propagation that incorporates network topology to improve clustering quality, especially in datasets with known network relations, outperforming traditional methods.

Contribution

The paper presents a novel geometric-AP algorithm that integrates network topology into exemplar-based clustering, enhancing performance over existing affinity propagation methods.

Findings

Significant improvement in clustering quality over benchmarks.

Geometric-AP performs well even when original AP fails.

Effective use of network constraints enhances clustering results.

Abstract

Clustering data into meaningful subsets is a major task in scientific data analysis. To date, various strategies ranging from model-based approaches to data-driven schemes, have been devised for efficient and accurate clustering. One important class of clustering methods that is of a particular interest is the class of exemplar-based approaches. This interest primarily stems from the amount of compressed information encoded in these exemplars that effectively reflect the major characteristics of the respective clusters. Affinity propagation (AP) has proven to be a powerful exemplar-based approach that refines the set of optimal exemplars by iterative pairwise message updates. However, a critical limitation is its inability to capitalize on known networked relations between data points often available for various scientific datasets. To mitigate this shortcoming, we propose geometric-AP, a novel clustering algorithm that effectively extends AP to take advantage of the network topology. Geometric-AP obeys network constraints and uses max-sum belief propagation to leverage the available network topology for generating smooth clusters over the network. Extensive performance assessment reveals a significant enhancement in the quality of the clustering results when compared to benchmark clustering schemes. Especially, we demonstrate that geometric-AP performs extremely well even in cases where the original AP fails drastically.