Evolutionary Clustering via Message Passing
This addresses the need for evolutionary clustering to analyze temporal changes in cluster memberships, though it appears incremental as an extension of affinity propagation to time-evolving data.
The paper tackles the problem of clustering objects that evolve over time by introducing evolutionary affinity propagation (EAP), which groups data points via message passing on a factor graph to ensure temporal smoothness and automatically determine and track clusters, showing effectiveness in comparisons on simulated and experimental data.
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.