Semi-supervised hierarchical optimization-based affinity propagation algorithm and its applications

Abstract

Attributable to the fact that the clustering accuracy is not high under the original Affinity Propagation (AP) algorithm, that is greatly affected by preference (P) when adjusting it to obtain the true class number of clustering, this paper proposed an improved AP algorithm named Semi-supervised Hierarchical Optimization-based AP algorithm (SHO-AP). The algorithm introduces the idea of semi-supervision, by setting a certain proportion of label data and using the AP to cluster, then establish the supervision and non-supervision information matrix to optimize, and combine the result of AP algorithm. We utilize hierarchical optimization to combine the final clustering results. The result of the experiment on UCI data-sets shows that the proposed algorithm achieves higher quality than the traditional AP algorithm and the number of classes is much closer to the real number.