K-NN Classifier Performs Better Than K-Means Clustering in Missing Value Imputation

Missing Data is a widespread problem that can affect the ability to use data to construct effective predictions systems.We analyze the predictive performance by comparing K-Means Clustering with kNN Classifier for imputing missing value.For investigation, we simulate with 5 missing data percentages; we found that k-NN performs better than K-Means Clustering, in terms of accuracy.