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Performance Evaluation of Missing-Value Imputation Clustering Based on a Multivariate Gaussian Mixture Model

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  • Jing Xiao
  • Qiongqiong Xu
  • Chuanli Wu
  • Yuexia Gao
  • Tianqi Hua
  • Chenwu Xu

Abstract

Background: It is challenging to deal with mixture models when missing values occur in clustering datasets. Methods and Results: We propose a dynamic clustering algorithm based on a multivariate Gaussian mixture model that efficiently imputes missing values to generate a “pseudo-complete” dataset. Parameters from different clusters and missing values are estimated according to the maximum likelihood implemented with an expectation-maximization algorithm, and multivariate individuals are clustered with Bayesian posterior probability. A simulation showed that our proposed method has a fast convergence speed and it accurately estimates missing values. Our proposed algorithm was further validated with Fisher’s Iris dataset, the Yeast Cell-cycle Gene-expression dataset, and the CIFAR-10 images dataset. The results indicate that our algorithm offers highly accurate clustering, comparable to that using a complete dataset without missing values. Furthermore, our algorithm resulted in a lower misjudgment rate than both clustering algorithms with missing data deleted and with missing-value imputation by mean replacement. Conclusion: We demonstrate that our missing-value imputation clustering algorithm is feasible and superior to both of these other clustering algorithms in certain situations.

Suggested Citation

  • Jing Xiao & Qiongqiong Xu & Chuanli Wu & Yuexia Gao & Tianqi Hua & Chenwu Xu, 2016. "Performance Evaluation of Missing-Value Imputation Clustering Based on a Multivariate Gaussian Mixture Model," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-14, August.
  • Handle: RePEc:plo:pone00:0161112
    DOI: 10.1371/journal.pone.0161112
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    References listed on IDEAS

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    1. Hunt, Lynette & Jorgensen, Murray, 2003. "Mixture model clustering for mixed data with missing information," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 429-440, January.
    2. J. A. Hartigan & M. A. Wong, 1979. "A K‐Means Clustering Algorithm," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(1), pages 100-108, March.
    3. Podofillini, L. & Zio, E. & Mercurio, D. & Dang, V.N., 2010. "Dynamic safety assessment: Scenario identification via a possibilistic clustering approach," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 534-549.
    4. Matthew Hayes & Yoon Soo Pyon & Jing Li, 2012. "A Model-Based Clustering Method for Genomic Structural Variant Prediction and Genotyping Using Paired-End Sequencing Data," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-13, December.
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