Evolutionary Clustering via Message Passing

TL;DR

This paper introduces EAP, an evolutionary clustering algorithm that uses message passing on a factor graph to automatically determine and track clusters over time, providing smooth and consistent clustering results for evolving data.

Contribution

The paper presents EAP, a novel evolutionary affinity propagation method that automatically determines the number of clusters and tracks their evolution without extra processing.

Findings

EAP effectively tracks cluster evolution in simulated data.

EAP outperforms existing methods in experimental data.

EAP determines the number of clusters automatically.

Abstract

We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently clustering data collected at different time points. In this paper we introduce evolutionary affinity propagation (EAP), an evolutionary clustering algorithm that groups data points by exchanging messages on a factor graph. EAP promotes temporal smoothness of the solution to clustering time-evolving data by linking the nodes of the factor graph that are associated with adjacent data snapshots, and introduces consensus nodes to enable cluster tracking and identification of cluster births and deaths. Unlike existing evolutionary clustering methods that require additional processing to approximate the number of clusters or match them across time, EAP determines the number of clusters and tracks them automatically. A comparison with existing methods on simulated and experimental data demonstrates effectiveness of the proposed EAP algorithm.