A Preference Model on Adaptive Affinity Propagation

Affinity Propagation (AP) is one of clustering technique that use iterative message passing and consider all data points as potential exemplars. Two important inputs of AP are a similarity matrix (SM) of the data and the parameter "preference" $p$. Although the standard AP algorithm has shown much success in data clustering, it still suffer from one limitation: it is not easy to determine the value of the parameter "preference" $p$ which can result an optimal clustering solution. To resolve this limitation, we propose a new model of the parameter "preference" $p$, i.e. it is modeled based on the similarity distribution. Having the SM and $p$, modified Adaptive AP (AAP) procedure is running. Modified AAP procedure means that we omit the adaptive p-scanning algorithm as in original AAP procedure. Experimental results on random non-partition and partition data sets show that (i) the proposed algorithm, AAP-DDP, is slower than original AP for random non-partition dataset, (ii) for random 4-partition dataset the proposed algorithm has succeeded in producing clusters according to the number of dataset's true labels with the execution times that are comparable with those original AP. Beside that the AAP-DDP algorithm demonstrates more feasible and effective than original AAP procedure.