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 for new development as well as the use of existing methods. This paper aims at a systematic study of discretization methods with their history of development, effect on classification, and trade-off between speed and accuracy. Contributions of this paper are an abstract description summarizing existing discretization methods, a hierarchical framework to categorize the existing methods and pave the way for further development, concise discussions of representative discretization methods, extensive experiments and their analysis, and some guidelines as to how to choose a discretization method under various circumstances. We also identify some issues yet to solve and future research for discretization.
- Continuous feature
- Data mining
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Computer Networks and Communications