K-Means Clustering with Build-in Missing Data Imputation

This k-means algorithm is able to cluster data with missing values and as a by-product completes the data set. The implementation can deal with missing values in multiple variables and is computationally efficient since it iteratively uses the current cluster assignment to define a plausible distribution for missing value imputation. Weights are used to shrink early random draws for missing values (i.e., draws based on the cluster assignments after few iterations) towards the global mean of each feature. This shrinkage slowly fades out after a fixed number of iterations to reflect the increasing credibility of cluster assignments. See the vignette for details.


Reference manual

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0.2.4 by Oliver Pfaffel, 8 months ago

Browse source code at https://github.com/cran/ClustImpute

Authors: Oliver Pfaffel

Documentation:   PDF Manual  

Task views:

GPL-3 license

Imports ClusterR, copula, dplyr, magrittr, tidyr, ggplot2, rlang, knitr

Suggests ggExtra, rmarkdown, testthat, Hmisc, tictoc, spelling, corrplot, covr

Suggested by FeatureImpCluster.

See at CRAN