Using Clustering and a Hybrid Method to Fill the Numeric Missing Values

Estimation of missing values is an important step in the preprocessing. In this paper, at two-step approach is proposed to fill the numeric missing values. In the first step, data is clustered. In the second step, the missing data in each cluster are estimated using a combination of weighted k nearest neighbors and linear regression methods. The correlation measure is employed to determine the appropriate method for the filling of missing data in each cluster. The quality of estimated missing values is evaluated using the root mean squared error (RMSE) criterion. Effect of different input parameters on the error of estimated values is investigated. Moreover, the performance of the proposed method for the estimation purpose is evaluated on five datasets. Finally, the efficiency of the proposed method is compared to four different estimation methods, namely, Mean estimation, multi-layer perceptron (MLP) based estimation, fuzzy C-means (FCM) based approximation method, and Class-based K-clusters nearest neighbor imputation (CKNNI) method. Experimental results show that the proposed method produces less error in comparison to other compared methods, in most of the cases.