Cluster Ensemble Algorithm Using Affinity Propagation

The result of K-means cluster is instable for random initial clustering centers.A cluster ensemble algorithm based on affinity propagation is proposed,where the result of each cluster individual is regarded as a property of the original data.Following the new properties sets,the results of each cluster individual are carried out to a weighted ensemble,and simple and efficient affinity propagation cluster is chosen in the ensemble algorithm.Furthermore the direct ensemble,the ensemble to weighted ensemble from average normalized mutual information(NMI) and cluster validation indexes Silhouette are uniformly proposed.Finally,Hungarian algorithm is employed to unify and match the category labels for the results of affinity propagation cluster.The results of experiments on University of California Irvine data sets show the higher efficiency for improving the accuracy,robustness and stability of cluster results than the K means clustering before combination and the other clustering ensemble algorithms.The clustering ensemble algorithm gets more extendable and flexible.