Semi-supervised affinity propagation clustering algorithm based on kernel function

Aiming at complex data sets, affinity propagation clustering algorithm has shortcomings of clustering inefficiency and low accuracy. A semi-supervised affinity propagation clustering algorithm based on kernel function (K-SAP Clustering Algorithm) is proposed in this paper. This algorithm first maps the complex clustering space into the feature space and change the similarity measure by a kernel function. Then semi-supervised algorithm is used to adjust the similarity matrix to be neighbours of data in same cluster. Finally, AP algorithm is used to iterate and undate to get the global optimum. Simulation results show the proposed algorithm is better and more accurate than SAP algorithm for complex data sets clustering.