DATA MINING TECHNIQUES FOR MISSING VALUE IMPUTATION
Mohamed, M., Hashem, A., AbdelSamea, M. (2010). DATA MINING TECHNIQUES FOR MISSING VALUE IMPUTATION. EKB Journal Management System, 38(No 4), 1001-1012. doi: 10.21608/jesaun.2010.125559
Marghny H. Mohamed; Abdel-Rahiem A. Hashem; M. M. AbdelSamea. "DATA MINING TECHNIQUES FOR MISSING VALUE IMPUTATION". EKB Journal Management System, 38, No 4, 2010, 1001-1012. doi: 10.21608/jesaun.2010.125559
Mohamed, M., Hashem, A., AbdelSamea, M. (2010). 'DATA MINING TECHNIQUES FOR MISSING VALUE IMPUTATION', EKB Journal Management System, 38(No 4), pp. 1001-1012. doi: 10.21608/jesaun.2010.125559
Mohamed, M., Hashem, A., AbdelSamea, M. DATA MINING TECHNIQUES FOR MISSING VALUE IMPUTATION. EKB Journal Management System, 2010; 38(No 4): 1001-1012. doi: 10.21608/jesaun.2010.125559

DATA MINING TECHNIQUES FOR MISSING VALUE IMPUTATION

JES. Journal of Engineering Sciences
Article 9, Volume 38, No 4, July and August 2010, Page 1001-1012  XML PDF (668.38 K)
Document Type: Research Paper
DOI: 10.21608/jesaun.2010.125559
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Authors
Marghny H. Mohamed1; Abdel-Rahiem A. Hashem2; M. M. AbdelSamea2
1Faculty of computers and Information, Assiut University, Egypt
2Faculty of Science Assiut University, Egypt
Abstract
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.
Keywords
Imputation; Clustering; K-mean
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