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

By Ms. R. Malarvizhi. 2012

33 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Anil S. Jadhav
2. Krishnan Ramanathan
3. Dhanya Pramod

In Statistics and Probability

1. Anil S. Jadhav
2. Krishnan Ramanathan
3. Dhanya Pramod

Citations in the corpus

• Comparison of Performance of Data Imputation Methods for Numeric Dataset (2019, methodology): “In a comparative study of single imputation methods, Malarvizhi and Thanamani (2012) found that median or standard deviation substitution perform better than mean substitution.”