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Data serves as an asset in the present era of information in almost all fields of study. The quality of the knowledge extracted, learning and decision problems depends upon the quality of data gathered. But most of the real world datasets tends to be incomplete. The problem of missing data can have a significant effect on the conclusions that can be drawn from this data. Data mining has made a great progress in recent year but the problem of missing data or value has still remained a great challenge for data mining. Missing data or value in a dataset can affect the performance of classifier which leads to difficulty of extracting useful information from datasets. In this paper we present the major challenges in predicting missing data in a data set and the existing approaches dealing with the missing data imputation. [ABSTRACT FROM AUTHOR]

Missing values occur frequently in various field experiments and trials of data. These missing values in a dataset pose challenges for the data miners and analysts working on that dataset. Hence knowing how to predict those missing values is important. The process of replacing missing value with the predicted value is called Imputation. In this paper we propose an Imputation method to predict the missing values based on supervised learning classification scheme. The proposed method first maps the missing value problem into a classification problem by discretization of the known available values. Further we make use of C 4.5 decision tree algorithm for prediction of the discrete nominal values corresponding to the missing values. Finally we predict the numeric values for the missing places using Local Closet Fit algorithm where the term local is defined by the discretization of the known values of the attribute with missing values. The performance of the proposed method is compared with the existing schemes for data imputation where the results show that the proposed method has higher prediction accuracy. [ABSTRACT FROM AUTHOR]