An Analysis on K-Means Algorithm as an Imputation Method to Deal with Missing Values

Imputation is a class of procedures that aims to fill the missing values with estimated ones. This method involves replacing missing values with estimated ones based on some information available in the data set. There are many options varying from naive methods like mean or mode imputation to some learning methods like 4.5 C based on relationships among attributes. In this research the use of K-Means algorithm is analyzed as a new approach to treat missing values. This research is to evaluate the efficiency of K-Means imputation algorithm as an imputation method to treat missing data, comparing its performance with the performance obtained by Mean, Median, Mode and 4.5 C.