Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (5): 165-169.doi: 10.11896/jsjkx.210800218

• Database & Big Data & Data Science • Previous Articles     Next Articles

Study on Affinity Propagation Clustering Algorithm Based on Bacterial Flora Optimization

ZHANG Yu-jiao1, HUANG Rui2, ZHANG Fu-quan2, SUI Dong3, ZHANG Hu4   

  1. 1 Academic Affairs Office,Taiyuan Normal University,Jinzhong,Shanxi 030619,China
    2 School of Computer Science,Beijing Institute of Technology,Beijing 100081,China
    3 School of Electrical and Information Engineering,Beijing University of Civil Engineering and Architecture,Beijing 102406,China
    4 School of Computer and Information Technology (School of Big Data),Shanxi University,Taiyuan 030006,China
  • Received:2021-08-25 Revised:2021-10-30 Online:2022-05-15 Published:2022-05-06
  • About author:ZHANG Yu-jiao,born in 1987,postgraduate.Her main research interests include artificial intelligence,big data analysis and computer education.
  • Supported by:
    National Natural Science Foundation of China(61871204) and National Natural Science Youth Fund(61702026).

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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.

Key words: Affinity propagation, Bacterial flora optimization, Bias parameter, Clustering

CLC Number: 

  • TP391
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