Data Mining Discretization Methods and Performances

Abstract

Discretization process is known to be one of the most important data preprocessing tasks in data mining. Presently, many discretization methods are available. These include Boolean Reasoning, Equal Frequency Binning, Entropy, and others. Each method is developed for specific problems or domain area. In consequent, the usage of such methods in other areas might not be appropriate. In appropriately used of a technique will cause serious problem to happen in which important data is lost. This will cause inaccuracy of results and unreliable models be produced. This study attempts to evaluate the performances of various discretization methods on several domain areas. The experiments have been validated using 10- fold cross validation method. The ranking of the performances of the methods have been discovered from the experiments. The results suggest that different discretization methods perform better in one or more domain areas.