Evolutionary affinity propagation

Natalia M. Arzeno, Haris Vikalo

Abstract: Evolutionary affinity propagation, an evolutionary clustering algorithm that groups data points by exchanging messages on a factor graph, is proposed. The algorithm promotes temporal smoothness of the clustering solutions at distinct temporal snapshots by linking variable nodes of the graph across time, and is capable of detecting cluster births and deaths. Unlike most existing evolutionary clustering methods that require additional processing in order to approximate the number of clusters, evolutionary affinity propagation determines the number of clusters automatically. A comparison with existing methods on simulated and experimental data demonstrates accuracy and robustness of the proposed framework.