Study on Affinity Propagation Clustering Algorithm Based on Bacterial Flora Optimization

Abstract

In order to improve the clustering performance of the nearest neighbor propagation clustering algorithm,the flora algorithm is used to optimize the parameters of the nearest neighbor propagation bias.Firstly,the similarity matrix is established according to the samples to be clustered,and the bias parameters are initialized.Secondly,the bias parameters are optimized by flora algorithm,which is used as colony for training,and the Silhouette index value is set as fitness function of flora algorithm.Then,the optimized bias parameters are updated by colony position to perform neighbor propagation clustering operation,and the decision and potential matrix of neighbor propagation clustering algorithm are continuously updated.Finally,stable clustering results are obtained.Experimental results show that better clustering results can be obtained by setting the parameters of flora optimization algorithm reasonably.Compared with common clustering algorithms,the proposed algorithm can obtain higher Silhouette index value and the shortest Euclidean distance performance in e-commerce dataset and UCI dataset,and has high applicability in clustering analysis.

Keywords

• affinity propagation|clustering|bacterial flora optimization|bias parameter