Mapping Missing Data Items Values Using Cluster Based Nearest Neighborhood Method

Many existing industrial and research data sets contain missing values due to various reasons, such as manual data entry procedures, equipment errors and incorrect measurements. Problems associated with missing values are loss of efficiency, complications in handling and analyzing the data and bias resulting from differences between missing and complete data. In this research Mean, Mode, Median and Cluster based nearest neighborhood imputation are used to fill the missing values. This method is used to map a data item to a real valued prediction variable. In a dataset if one attributes is depending on other by using known values the unknown values will be predicted.