Discretization: An Enabling Technique

Discrete values have important roles in data mining and knowledge discovery. They are about intervals of numbers which are more concise to represent and specify, easier to use and comprehend as they are closer to a knowledge-level representation than continuous values. Many studies show induction tasks can benefit from discretization: rules with discrete values are normally shorter and more understandable and discretization can lead to improved predictive accuracy. Furthermore, many induction algorithms found in the literature require discrete features. All these prompt researchers and practitioners to discretize continuous features before or during a machine learning or data mining task. There are numerous discretization methods available in the literature. It is time for us to examine these seemingly different methods for discretization and find out how different they really are, what are the key components of a discretization process, how we can improve the current level of research...

• Type: Journal
• Year: 2002
• Where: DATAMINE
• Authors: Huan Liu, Farhad Hussain, Chew Lim Tan, Manoranjan Dash
• Keywords: DATAMINE 2002, Discretization, Discretization Method, Numerous Discretization Methods