Imputation is a class of procedures that aims to fill the values which are missed with estimated ones. These methods involve replacing missing values with estimated ones based on some information available in the data set. K-means has been successful in finding missing values for several data sets available such as Bupa, Breast Cancer, Pima, etc. In this paper, we introduce an efficient imputation methods based K-means to treat missing data. Our proposed methods give higher accuracy than the one on given by classical K-means. Experimental results hold on a variety class of data sets.
Mohamed, M. H., Hashem, A. A., & AbdelSamea, M. M. (2010). DATA MINING TECHNIQUES FOR MISSING VALUE IMPUTATION. JES. Journal of Engineering Sciences, 38(No 4), 1001-1012. doi: 10.21608/jesaun.2010.125559
MLA
Marghny H. Mohamed; Abdel-Rahiem A. Hashem; M. M. AbdelSamea. "DATA MINING TECHNIQUES FOR MISSING VALUE IMPUTATION", JES. Journal of Engineering Sciences, 38, No 4, 2010, 1001-1012. doi: 10.21608/jesaun.2010.125559
HARVARD
Mohamed, M. H., Hashem, A. A., AbdelSamea, M. M. (2010). 'DATA MINING TECHNIQUES FOR MISSING VALUE IMPUTATION', JES. Journal of Engineering Sciences, 38(No 4), pp. 1001-1012. doi: 10.21608/jesaun.2010.125559
VANCOUVER
Mohamed, M. H., Hashem, A. A., AbdelSamea, M. M. DATA MINING TECHNIQUES FOR MISSING VALUE IMPUTATION. JES. Journal of Engineering Sciences, 2010; 38(No 4): 1001-1012. doi: 10.21608/jesaun.2010.125559
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