Research Output
SMK-means: An Improved Mini Batch K-means Algorithm Based on Mapreduce with Big Data
  In recent years, the rapid development of big data technology has also been favored by more and more scholars. Massive data storage and calculation problems have also been solved. At the same time, outlier detection problems in mass data have also come along with it. Therefore, more research work has been devoted to the problem of outlier detection in big data. However, the existing available methods have high computation time, the improved algorithm of outlier detection is presented, which has higher performance to detect outlier. In this paper, an improved algorithm is proposed. The SMK-means is a fusion algorithm which is achieved by Mini Batch K-means based on simulated annealing algorithm for anomalous detection of massive household electricity data, which can give the number of clusters and reduce the number of iterations and improve the accuracy of clustering. In this paper, several experiments are performed to compare and analyze multiple performances of the algorithm. Through analysis, we know that the proposed algorithm is superior to the existing algorithms.

  • Type:


  • Date:

    01 December 2018

  • Publication Status:


  • DOI:


  • ISSN:


  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    005.8 Data security

  • Funders:

    European Commission


Xiao, B., Wang, Z., Liu, Q., & Liu, X. (2018). SMK-means: An Improved Mini Batch K-means Algorithm Based on Mapreduce with Big Data. Computers, Materials & Continua, 56(3), 365-379.



Big data, outlier detection, SMK-means, Mini Batch K-means, simulated annealing.

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