Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (10): 126-129.doi: 10.11896/jsjkx.190900113

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

K-mediods Cluster Mining and Parallel Optimization Based on Shuffled Frog Leaping Algorithm

WEI Lin-jing1, NING Lu-lu2, GUO Bin3, HOU Zhen-xing4, GAN Shi-run1   

  1. 1 School of Information Science & Technology,Gansu Agriculture University,Lanzhou 730070,China
    2 School of Light Industry Science & Engineering,Shaanxi University of Science and Technology,Xi’an 710021,China
    3 College of Computer and Information,Hohai University,Nanjing 210094,China
    4 School of Information Management,Nanjing University,Nanjing 210093,China
  • Received:2019-09-16 Revised:2019-10-21 Online:2020-10-15 Published:2020-10-16
  • About author:WEI Lin-jing,born in 1977,Ph.D,professor,master supervisor,is a member of China Computer Federation.Her main research interests include intelligent computing,algorithm application research and image analysis.
  • Supported by:
    Discipline Construction Project of Gansu Agricultural University (GAU-XKJS-2018-257),Youth Tutor Support Fund Project of Gansu Agricultural University (GAU-QDFC-2018-13),Lanzhou Science and Technology Plan Project (2019-1-31),Gansu Provincial Department of Education Innovation Fund (2020B-122) and National Natural Science Foundation of China (61063028,31560378)

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Abstract: In order to reduce the error of K-mediods clustering algorithm and improve the performance of parallel optimization,the shuffled frog leaping algorithm is applied to the clustering and parallel optimization process.In the K-mediods clustering process,K-mediods is combined with the clustering cluster idea to optimize the shuffled frog leaping algorithm for each cluster cluster,which improves the efficiency of large-scale data sample clustering,especially for multiple clusters.When the class execution nodes complete the large-scale sample K-mediods clustering in parallel,the shuffled frog leaping algorithm effectively improves the speedup ratio.It has been proved by experiments that the K-mediods clustering based on the shuffled frog leaping algorithm has obvious clustering advantages compared with the common K-mediods clustering,and the acceleration ratio performance of processing large-scale samples is better.

Key words: Acceleration ratio, Clustering cluster, K-mediods clustering, Parallel optimization, Shuffled frog leaping algorithm

CLC Number: 

  • TP181
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