Data Mining: A Preprocessing Engine

Basel Kasasbeh,Luai Al Shalabi,Zyad Shaaban

doi:10.3844/jcssp.2006.735.739

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 new matrix was designed to check for the best normalization method based on the factors and their priorities. Recommendations were concluded.

Highlights

Induction decision tree (ID3)[1] algorithm implements a scheme for top-down induction of decision trees using depth-first search
Example, rules generation technique could give low accuracy when it is applied to decimal scaling normalization data set, while it gives much better accuracy when it is applied to z-score or min-max normalization data sets
We studied the three different normalization methods

Summary

INTRODUCTION

Induction decision tree (ID3)[1] algorithm implements a scheme for top-down induction of decision trees using depth-first search. The growth of the size of data and number of existing databases exceeds the ability of humans to analyze this data, which creates both a need and an opportunity to extract knowledge from databases[3] Data transformation such as normalization may improve the accuracy and efficiency of mining algorithms involving neural networks, nearest neighbor and clustering classifiers. Such methods provide better results if the data to be analyzed have been normalized, that is, scaled to specific ranges such as [0.0, 1.0][2]. Normalization can change the original data and it is necessary to save the normalization parameters (the mean and the standard deviation if using the z-score normalization and the minimum and the maximum values if using the min-max normalization) so that future data can be normalized in the same manner

METHODOLOGY

RESULTS AND DISCUSSION

CONCLUSION