Geometric Affinity Propagation for Clustering With Network Knowledge

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 corresponding 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 address this shortcoming, we propose Geometric-AP, a novel clustering algorithm that effectively extends the original 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 shows that Geometric-AP leads to a significant quality enhancement of the clustering results when compared to existing schemes. Especially, we demonstrate that Geometric-AP performs extremely well even in cases where the original AP fails drastically.