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Incomplete high-dimensional data imputation algorithm using feature selection and clustering analysis on cloud

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Abstract

Incomplete data imputation plays an important role in big data analysis and smart computing. Existing algorithms are of low efficiency and effectiveness in imputing incomplete high-dimensional data. The paper proposes an incomplete high-dimensional data imputation algorithm based on feature selection and cluster analysis (IHDIFC), which works in three steps. First, a hierarchical clustering-based feature subset selection algorithm is designed to reduce the dimensions of the data set. Second, a parallel \(k\)-means algorithm based on partial distance is derived to cluster the selected data subset efficiently. Finally, the data objects in the same cluster with the target are utilized to estimate its missing feature values. Extensive experiments are carried out to compare IHDIFC to two representative missing data imputation algorithms, namely FIMUS and DMI. The results demonstrate that the proposed algorithm achieves better imputation accuracy and takes significantly less time than other algorithms for imputing high-dimensional data.

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Authors and Affiliations

  1. School of Software Technology, Dalian University of Technology, Dalian, 116620, China

    Fanyu Bu, Zhikui Chen & Qingchen Zhang

  2. College of Vocation, Inner Mongolia University of Finance and Economics, Hohhot, 010010, China

    Fanyu Bu

  3. Department of Computer Science, St. Francis Xavier University, Antigonish, B2G 2W5, Canada

    Laurence T. Yang

Authors
  1. Fanyu Bu
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  2. Zhikui Chen
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  3. Qingchen Zhang
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  4. Laurence T. Yang
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Corresponding author

Correspondence to Qingchen Zhang.

Additional information

This work was supported by Project U1301253 of NSFC and Project 201202032 of Liaoning Provincial Natural Science Foundation of China.

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Bu, F., Chen, Z., Zhang, Q. et al. Incomplete high-dimensional data imputation algorithm using feature selection and clustering analysis on cloud. J Supercomput 72, 2977–2990 (2016). https://doi.org/10.1007/s11227-015-1433-9

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  • DOI: https://doi.org/10.1007/s11227-015-1433-9

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