Some Imputation Methods to Treat Missing Values in Knowledge Discovery in Data Warehouse

One major problem in the data cleaning & data reduction step of KDD process is the presence of missing values in attributes. Many of analysis task have to deal with missing values and have developed several treatments to guess them. One of the most common method to replace the missing values is the mean method of imputation. In this paper the authors suggested a new imputation method by combining factor type and compromised imputation method, using two-phase sampling scheme and by using this method they impute the missing values of a target attribute in a data warehouse. The simulation study shows that the estimator of mean from this method is found more efficient than compare to other.