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

Jing Xiao, Qiongqiong Xu, Chuanli Wu, Yuexia Gao, Tianqi Hua and Chenwu Xu

PLOS ONE, 2016, vol. 11, issue 8, 1-14

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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0161112

DOI: 10.1371/journal.pone.0161112

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