A Generalized Affinity Propagation Clustering Algorithm for Nonspherical Cluster Discovery
Clustering analysis aims to discover the underlying clusters in the data points according to their similarities. It has wide applications ranging from bioinformatics to astronomy. Here, we proposed a Generalized Affinity Propagation (G-AP) clustering algorithm. Data points are first organized in a sparsely connected in-tree (IT) structure by a physically inspired strategy. Then, additional edges are added to the IT structure for those reachable nodes. This expanded structure is subsequently trimmed by affinity propagation method. Consequently, the underlying cluster structure, with separate clusters, emerges. In contrast to other IT-based methods, G-AP is fully automatic and takes as input the pairs of similarities between data points only. Unlike affinity propagation, G-AP is capable of discovering nonspherical clusters.