A New Imputation Method for Missing Attribute Values in Data Mining

Abstract

One reduction problem in the data cleaning & data reduction step of KDD process is the presence of missing values in attributes. Many of analysis tasks have proposed to deal with missing values and have developed several treatments to guess them. One of the most common methods to replace the missing values is the mean method of imputation. In this paper we suggest a new imputation method using modified ratio estimator in two phase sampling scheme and by using this method, we input the missing values of a target attribute in a data warehouse. Our simulation study shows that the estimator of mean from this method is found more efficient than compare to other imputation methods.

Keywords

• KDD
• Knowledge Discovery In Databases
• Data Mining Attribute Missing Values
• Imputation Methods
• Sampling