title: Performance Evaluation of Missing-Value Imputation Clustering Based on a Multivariate Gaussian Mixture Model.
abstract: BACKGROUND
  It is challenging to deal with mixture models when missing values occur in clustering datasets.
  
  
  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.
  
  
  CONCLUSIONS
  We demonstrate that our missing-value imputation clustering algorithm is feasible and superior to both of these other clustering algorithms in certain situations.
authors.affiliation: Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, 226019, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Xiao
authors.affiliation: Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, 226019, China.
authors.forename: Qiongqiong
authors.initials: Q
authors.lastname: Xu
authors.affiliation: Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, 226019, China.
authors.forename: Chuanli
authors.initials: C
authors.lastname: Wu
authors.affiliation: Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, 226019, China.
authors.forename: Yuexia
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, 226019, China.
authors.forename: Tianqi
authors.initials: T
authors.lastname: Hua
authors.affiliation: Jiangsu Key Laboratory of Crop Genetics and Physiology/Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of the Ministry of Education, Yangzhou University, Yangzhou, 225009, China.
authors.forename: Chenwu
authors.initials: C
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D008962:Models, Theoretical; D016011:Normal Distribution