A local approach of adaptive affinity propagation clustering for large scale data

Affinity propagation exhibits fast execution speed and finds clusters with low error rate when clustering sparsely related data but its values of parameters are fixed. This paper proposes a modified method named partition adaptive affinity propagation, which can automatically eliminate oscillations and adjust the values of parameters when rerunning affinity propagation procedure to yield optimal clustering results, with high execution speed and precision. Experiments are carried on UCI datasets and Caltech101 dataset, and ORL faces dataset. The results verify that this adaptive method is effective and feasible.