K-AP: Generating Specified K Clusters by Efficient Affinity Propagation

Abstract--The Affinity Propagation (AP) clustering algorithm proposed by Frey and Dueck (2007) provides an understandable, nearly optimal summary of a data set. However, it suffers two major shortcomings: i) the number of clusters is vague with the user-defined parameter called self-confidence, and ii) the quadratic computational complexity. When aiming at a given number of clusters due to prior knowledge, AP has to be launched many times until an appropriate setting of self-confidence is found. The re-launched AP increases the computational cost by 1 order of magnitude. In this paper, we propose an algorithm, called K-AP, to exploit the immediate results of K clusters by introducing a constraint in the process of message passing. Through theoretical analysis and experimental validation, K-AP was shown to be able to directly generate K clusters as user defined, with a negligible increase of computational cost compared to AP. In the meanwhile, KAP preserves the clustering quality as AP ...

• Type: Journal
• Year: 2010
• Where: ICDM
• Authors: Xiangliang Zhang, Wei Wang, Kjetil Nørvåg, Michèle Sebag
• Keywords: Affinity Propagation, Computational Cost, Data Mining, ICDM 2010, Quadratic Computational Complexity