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 one 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, K-AP preserves the clustering quality as AP in terms of the distortion. K-AP is more effective than k-medoids w.r.t. the distortion minimization and higher clustering purity. © 2010 IEEE.
|Original language||English (US)|
|Title of host publication||2010 IEEE International Conference on Data Mining|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||6|
|State||Published - Dec 2010|