Classification of Efficient Imputation Method for Analyzing Missing Values
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2014 by IJCTT Journal|
|Year of Publication : 2014|
|Authors : S.Kanchana , Dr. Antony Selvadoss Thanamani|
|DOI : 10.14445/22312803/IJCTT-V12P138|
S.Kanchana , Dr. Antony Selvadoss Thanamani."Classification of Efficient Imputation Method for Analyzing Missing Values". International Journal of Computer Trends and Technology (IJCTT) V12(4):193-195, June 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
In Statistical analysis, missing data is a common problem for data quality. Many real datasets have missing data. Imputation preserves all cases by replacing missing data with a probable value based on other available information. Once all missing values have been imputed, the data set can be analyzed using standard techniques for complete data. This paper aim is to describe the efficient imputation method like Mean, Median, Refined Mean, Standard Deviation, Linear Regression, Discretization based method and some of clustering techniques like K-Mean and KNN methods which are used for imputing missing values in the dataset. The datasets are taken from the UCI ML repository. The results are compared in terms of accuracy.
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Clustering Techniques, Discretization, K-Mean, KNN, Mean, Median, Refined Mean, Standard Deviation.