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).

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, Clustering, Bacterial flora optimization, Bias parameter

CLC Number: 

  • TP391
[1]ZHANG Y L,ZHOU Y J.Overview of clustering algorithms[J].Computer Applications,2019,39 (7):1869-1882.
[2]HU F,CHEN H,WANG X.An Intuitionistic Kernel-BasedFuzzy C-Means Clustering Algorithm with Local Information for Power Equipment Image Segmentation[J].IEEE Access,2020,8:4500-4514.
[3]QIN Y B,SUN Y J,WEI X.Microblog user interest miningmethod based on text clustering and interest attenuation[J].Computer Application Research,2019,36 (5):1469-1473.
[4]XIE J Y,DING L J.Fully adaptive spectral clustering algorithm[J].Acta Electronica Sinica,2019,435 (5):26-34.
[5]XUE L X,SUN W,WANG R G,et al.Spectral clustering algorithm based on density peak optimization[J].Computer Application Research,2019,36(7):1948-1950.
[6]OLSON C F.Parallel algorithms for hierarchical clustering[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2016,12(11):1088-1092.
[7]ALEJANDRO V S,AHMED A,MOHAMMED F B.Mathe-matical modeling and a hybridized bacterial foraging optimization algorithm for the flexible job-shop scheduling problem with sequencing flexibility[J].Journal of Manufacturing Systems,2020(54):74-93.
[8]LI F,JI W,TAN S,et al.Quantum Bacterial Foraging Optimization:From Theory to MIMO System Designs[J].IEEE Open Journal of the Communications Society,2020(1):1632-1646.
[9]XU Z,ZHUANG L,TIAN S,et al.Energy-driven Virtual Network Embedding Algorithm Based on Enhanced Bacterial Foraging Optimization[J].IEEE Access,2020,8:76069-76081.
[10]GUO J,GENG H J,WU Y.Research on K-means clustering algorithm based on flora optimization[J].Journal of Nanjing University of Science and Technology (Natural Science Edition),2021,45(3):314-319.
[11]WANG H L,ZHANG C G,TANG C C,et al.Optimizing the energy consumption of sensor network in production workshop based on flora optimization algorithm[J].Journal of Jinan University (Natural Science Edition),2021,35(4):370-375.
[12]LEI Q,LI T.Semi-Supervised Selective Affinity PropagationEnsemble Clustering with Active Constraints[J].IEEE Access,2020(8):46255-46266.
[13]ZHOU R,LIU Q,WANG J,et al.Modified semi-supervised affinity propagation clustering with fuzzy density fruit fly optimization[J].Neural Computing and Applications,2020(1):1-18.
[14]JIAO L,SHANG R,LIU F,et al.Fast clustering methods based on affinity propagation and density weighting[M]//Brain and Nature-Inspired Learning Computation and Recognition.2020:437-475.
[16]SUBEDI S,GANG H S,KO N Y,et al.Improving Indoor Fingerprinting Positioning With Affinity Propagation Clustering and Weighted Centroid Fingerprint[J].IEEE Access,2019,7:31738-31750.
[16]LIU X,XU Y,MONTES R,Et al.Alternative Ranking-Based Clustering and Reliability Index-Based Consensus Reaching Process for Hesitant Fuzzy Large Scale Group Decision Making[J].IEEE Transactions on Fuzzy Systems,2019,27(1):159-171.
[17]PARK S,JO H S,MUN C,et al.RRH Clustering Using Affinity Propagation Algorithm with Adaptive Thresholding and Greedy Merging in Cloud Radio Access Network[J].Sensors,2021,21(2):480-489.
[18]SMINESH C N,KANAGA E,SREEJISH A G.Augmented Affinity Propagation-Based Network Partitioning for Multiple Controllers Placement in Software Defined Networks[J].Journal of Computational and Theoretical Nanoscience,2020,17(1):228-233.
[19]LANIE B L.AFFINITY Propagation SMOTE approach for Imbalanced dataset used in Predicting Student at Risk of Low Performance[J].International Journal of Advanced Trends in Computer Science and Engineering,2020,9(4):5066-5070.
[20]TAHERI S,BOUYER A.Community Detection in Social Networks Using Affinity Propagation with Adaptive Similarity Matrix[J].Big Data,2020,8(3):11-19.
[21]YANG Y,DORN C.Affinity propagation clustering of full-field,high-spatial-dimensional measurements for robust output-only modal identification:A proof-of-concept study[J].Journal of Sound and Vibration,2020,483(2):115-123.
[22]FANTOUKH N I,ISMAIL M,BCHIR O.Automatic Determination of the Number of Clusters for Semi-Supervised Relational Fuzzy Clustering[J].International Journal of Fuzzy Logic and Intelligent Systems,2020,20(2):156-167.
[23]CHEN Y W,SHEN L L,ZHONG C M,et al.Overview of densi-ty peak clustering algorithms[J].Computer Research and Development,2020,07(2):378-394.
[24]HU S J,LU H Y,XIANG L,et al.Fuzzy clustering parthenogenetic algorithm for solving MMTSP[J].Computer Science,2020,47(6):219-224.
[25]HUANG X H,WANG C,XIONG L Y,et al.A weighted K-means clustering method integrating intra cluster and inter cluster distances[J].Chinese Journal of Computers,2019,42(12):248-260.
[1] XING Yun-bing, LONG Guang-yu, HU Chun-yu, HU Li-sha. Human Activity Recognition Method Based on Class Increment SVM [J]. Computer Science, 2022, 49(5): 78-83.
[2] ZHU Zhe-qing, GENG Hai-jun, QIAN Yu-hua. Line-Segment Clustering Algorithm for Chemical Structure [J]. Computer Science, 2022, 49(5): 113-119.
[3] ZUO Yuan-lin, GONG Yue-jiao, CHEN Wei-neng. Budget-aware Influence Maximization in Social Networks [J]. Computer Science, 2022, 49(4): 100-109.
[4] YANG Xu-hua, WANG Lei, YE Lei, ZHANG Duan, ZHOU Yan-bo, LONG Hai-xia. Complex Network Community Detection Algorithm Based on Node Similarity and Network Embedding [J]. Computer Science, 2022, 49(3): 121-128.
[5] HAN Jie, CHEN Jun-fen, LI Yan, ZHAN Ze-cong. Self-supervised Deep Clustering Algorithm Based on Self-attention [J]. Computer Science, 2022, 49(3): 134-143.
[6] PU Shi, ZHAO Wei-dong. Community Detection Algorithm for Dynamic Academic Network [J]. Computer Science, 2022, 49(1): 89-94.
[7] ZHANG Ya-di, SUN Yue, LIU Feng, ZHU Er-zhou. Study on Density Parameter and Center-Replacement Combined K-means and New Clustering Validity Index [J]. Computer Science, 2022, 49(1): 121-132.
[8] LUO Yue-tong, WANG Tao, YANG Meng-nan, ZHANG Yan-kong. Historical Driving Track Set Based Visual Vehicle Behavior Analytic Method [J]. Computer Science, 2021, 48(9): 86-94.
[9] DU Liang, REN Xin, ZHANG Hai-ying, ZHOU Peng. Multiple Kernel Clustering via Local Regression Integration [J]. Computer Science, 2021, 48(8): 47-52.
[10] QIAO Ying-jing, GAO Bao-lu, SHI Rui-xue, LIU Xuan, WANG Zhao-hui. Improved FCM Brain MRI Image Segmentation Algorithm Based on Tamura Texture Feature [J]. Computer Science, 2021, 48(8): 111-117.
[11] GAO Yan, YAN Qiu-yan, XIA Shi-xiong, ZHANG Zi-han. Interactive Group Discovery Based on Skeleton Trajectory Aggregation Model in ClassEnvironment [J]. Computer Science, 2021, 48(8): 334-339.
[12] ZHAO Min, LIU Jing-lei. Semi-supervised Clustering Based on Gaussian Fields and Adaptive Graph Regularization [J]. Computer Science, 2021, 48(7): 137-144.
[13] WU Shan-jie, WANG Xin. Prediction of Tectonic Coal Thickness Based on AGA-DBSCAN Optimized RBF Neural Networks [J]. Computer Science, 2021, 48(7): 308-315.
[14] YUAN Xiao-lei, YUE Xiao-feng, FANG Bo, MA Guo-yuan. Three-dimensional Target Recognition Method Based on Pair Point Feature and HierarchicalComplete-linkage Clustering [J]. Computer Science, 2021, 48(6A): 127-131.
[15] GUO Yi-shan, LIU Man-dan. Anomaly Detection Based on Spatial-temporal Trajectory Data [J]. Computer Science, 2021, 48(6A): 213-219.
Full text



[1] SHI Chao, XIE Zai-peng, LIU Han and LV Xin. Optimization of Container Deployment Strategy Based on Stable Matching[J]. Computer Science, 2018, 45(4): 131 -136 .
[2] ZHAO Xiao-yan, LIU Hong-zhe, YUAN Jia-zheng and YANG Shao-peng. Advances in Image Reranking[J]. Computer Science, 2018, 45(5): 15 -23 .
[3] LI Xiang-yuan, CAI Cheng, HE Jin-rong. Density Scaling Factor Based ISOMAP Algorithm[J]. Computer Science, 2018, 45(7): 207 -213 .
[4] YU Yong,KANG Qing-yi,CHEN Chang-geng,KAN Shi-lin,LUO Yong-jun. Bisecting K-means Clustering Method Based on Cohesion and Coupling[J]. Computer Science, 2018, 45(6A): 460 -464 .
[5] ZHOU Ming-quan, JIANG Guo-hua. New Spectrum-based Fault Localization Method Combining HittingSet and Genetic Algorithm[J]. Computer Science, 2018, 45(9): 207 -212 .
[6] WANG Jian, ZHANG Yang-sen, CHEN Ruo-yu, JIANG Yu-ru, YOU Jian-qing. Identification of User’s Role and Discovery Method of Its Malicious Access Behavior in Web Logs[J]. Computer Science, 2018, 45(10): 160 -165 .
[7] DING Mian-wei, ZHANG Teng-fei and MA Fu-min. Incremental Attribute Reduction Algorithm Based on Binary Discernibility Matrix in Incomplete Information System[J]. Computer Science, 2017, 44(7): 244 -250 .
[8] GUO Dong-yue and LIU Lin-feng. Opportunistic Routing Algorithm Based on Regional Friendship[J]. Computer Science, 2017, 44(3): 105 -109 .
[9] SHI Xue-kai, WANG Wen-ke, HUANG Hui, LI Si-kun and FU Yi-qi. Volume Rendering Method of Mass Brain Imaging Data Based on Compression Domain[J]. Computer Science, 2017, 44(3): 27 -31 .
[10] ZHANG Xiu,LI Nian-zu and LI Wei. Verification for Robustness of Chroma Feature[J]. Computer Science, 2014, 41(Z6): 24 -28 .