A Perspective Missing Values in Data Mining Applications
In large database there may be some values missing in some of the attributes. These missing values are calculated first by identifying it either discrete/continuous and then the values are calculated by mean, median. In this paper the calculated missing set values are utilized to estimate the imputation of missing values in data set. Methods are discussed for learning and application of decision rules for classification of data with many missing values. A method is presented to induce decision rules from data with missing values either by format of the rules is showing no different than with missing values or no special features are specified to prepare the original data.