Missing Value Prediction for Qualitative Information Systems

Abstract

Most information systems usually have some missing values due to unavailable data. Missing values minimizing the quality of classification rules generated by a data mining system. Missing values lead to the difficulty of extracting useful information from the data set. Solving the problem of missing data is a high priority in the field of data mining and knowledge discovery. The main goal of this paper is to introduce a method for converting qualitative information system into a binary system, by using a distance function between condition attributes, we can detect the missing values for decision attribute according to the smallest distance. Most common values can be used to solve the problem of repeated small distance for some cases. This method will be discussed in detail with an example of a case study.