K-MEAP: Generating Specified K Clusters with Multiple Exemplars by Efficient Affinity Propagation

Recently, an attractive clustering approach named multi-exemplar affinity propagation (MEAP) has been proposed as an extension to the single exemplar based Affinity Propagation (AP). MEAP is able to automatically identify multiple exemplars for each cluster associated with a super exemplar. However, if the cluster number is a prior knowledge and can be specified by the user, MEAP is unable to make use of such knowledge directly in its learning process. Instead it has to rely on re-running the process as many times as it takes by tuning parameters until it generates the desired number of clusters. The process of MEAP re-running may be very time consuming. In this paper, we propose a new clustering algorithm called KMEAP which is able to generate specified K clusters directly while retaining the advantages of MEAP. Two kinds of new additional messages are introduced in MEAP in order to control the number of clusters in the process of message passing. The detailed problem formulation, the derived updating rules for passing messages, and the in-depth analysis of the proposed K-MEAP are provided. Experimental studies demonstrated that K-MEAP not only generates K clusters directly and efficiently without tuning parameters, but also outperforms related approaches in terms of clustering accuracy.