基于菌群优化的近邻传播聚类算法研究  

Study on Affinity Propagation Clustering Algorithm Based on Bacterial Flora Optimization

作　　者：张宇姣 黄锐 张福泉 隋栋 张虎[4] ZHANG Yu-jiao;HUANG Rui;ZHANG Fu-quan;SUI Dong;ZHANG Hu(Academic Affairs Office,Taiyuan Normal University,Jinzhong,Shanxi 030619,China;School of Computer Science,Beijing Institute of Technology,Beijing 100081,China;School of Electrical and Information Engineering,Beijing University of Civil Engineering and Architecture,Beijing 102406,China;School of Computer and Information Technology(School of Big Data),Shanxi University,Taiyuan 030006,China)

机构地区：[1]太原师范学院教务处,山西晋中030619 [2]北京理工大学计算机学院,北京100081 [3]北京建筑大学电气与信息工程学院,北京102406 [4]山西大学计算机与信息技术学院(大数据学院),太原030006

出　　处：《计算机科学》2022年第5期165-169,共5页Computer Science

基　　金：国家自然科学基金面上项目(61871204);国家自然科学青年基金(61702026)。

摘　　要：为了提高近邻传播聚类算法的聚类性能,采用菌群算法进行近邻传播偏向参数优化求解。首先,根据待聚类样本建立相似矩阵,初始化偏向参数;然后采用菌群算法优化偏向参数,将偏向参数作为菌落进行训练,设置轮廓(Silhouette)指标值作为菌群算法的适应度函数;接着通过菌落位置更新优化后的偏向参数,进行近邻传播聚类运算,不断更新近邻传播聚类算法的决策和潜力阵;最后获得稳定的聚类结果。实验结果表明,合理设置菌群优化算法的参数,能够获得较好的聚类效果。在电商数据集和UCI数据集中,相比常用聚类算法,所提算法能够获得更高的Silhouette指标值和最短的欧氏距离,在聚类分析中的适用度较高。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.

关 键 词：近邻传播 聚类 菌群优化 偏向参数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]