  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.
