Extended Affinity Propagation: Global Discovery and Local Insights

TL;DR

Extended Affinity Propagation is a novel clustering algorithm that enhances global structure discovery and provides detailed local insights, maintaining the advantages of the original Affinity Propagation.

Contribution

It introduces modifications to Affinity Propagation to uncover global structures and offers additional internal cluster insights, improving interpretability and hyperparameter tuning.

Findings

Successfully discovers global data structures.

Provides detailed local cluster insights.

Performs well on synthetic and real datasets.

Abstract

We propose a new clustering algorithm, Extended Affinity Propagation, based on pairwise similarities. Extended Affinity Propagation is developed by modifying Affinity Propagation such that the desirable features of Affinity Propagation, e.g., exemplars, reasonable computational complexity and no need to specify number of clusters, are preserved while the shortcomings, e.g., the lack of global structure discovery, that limit the applicability of Affinity Propagation are overcome. Extended Affinity Propagation succeeds not only in achieving this goal but can also provide various additional insights into the internal structure of the individual clusters, e.g., refined confidence values, relative cluster densities and local cluster strength in different regions of a cluster, which are valuable for an analyst. We briefly discuss how these insights can help in easily tuning the hyperparameters. We also illustrate these desirable features and the performance of Extended Affinity Propagation on various synthetic and real world datasets.