Normalization as a Preprocessing Engine for Data Mining and the Approach of Preference Matrix

Abstract

This study is emphasized on different types of normalization. Each of which was tested against the ID3 methodology using the HSV data set. Number of leaf nodes, accuracy, and tree growing time are three factors that were taken into account. Comparisons between different learning methods were accomplished as they were applied to each normalization method. A simple matrix was designed to check for the best normalization method based on the factors and their priorities. Recommendations were concluded