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One relevant problem in data preprocessing is the presence of missing data that leads the poor quality of patterns, extracted after mining. Imputation is one of the widely used procedures that replace the missing values in a data set by some probable values. The advantage of this approach is that the missing data treatment is independent of the learning algorithm used. This allows the user to select the most suitable imputation method for each situation. This paper analyzes the variousdoi:10.24297/ijct.v11i7.3472 fatcat:57iwpcjkgbb5daz4bllvb5hggq