The problems of missing values in the field of data mining have become emerging areas of research in recent years. It has been a great challenge in research for quite a long time. The missing values may occur due to several reasons. The missing values in the data set can affect accuracy and performance of result when any algorithm is implemented on it. Presence of missing values leads to less efficiency and difficulty in extracting meaningful information. As we go through the literature we can find there are various imputation techniques basing on type of missing value. Since the amount of data is increasing day by day, there is a need for an appropriate technique to handle the missing values in the data set. In this paper a brief year wise study of existing methods are being done so that it would be a great help while formulating and implementing a new one towards solving the problem of missing values.

Data Mining, Data Set, Missing Values, Algorithm, Information, Imputation

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