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In data mining one important stage is pre-processing. In which there are different mining tasks for it. In real world most of the data are noisy, inconsistent and incorrect. In fact, the most important step in pre-processing is filling (or handling) missing value. Missing data imputation is an important step in the process of machine learning and data mining when certain values are missed. In this paper, the authors have presented comparative review of the imputation method basically which are used for imputing missing values in the dataset. They have discussed the parametric, non-parametric and semi-parametric imputation methods.
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