A Taxonomy of Discretization Techniques based on Class Labels and Attributes' Relationship

Abstract

Discretizing continuous attributes is one essential and important data preprocessing step in data mining. Various data mining techniques are designed to be applied to discrete attributes. There have been tremendous efforts to propose discretization techniques with different characteristics. However, a clear pathway that can guide the choice of the needed discretization technique for different types of datasets is lacking. This paper proposes a taxonomy based on the existence of class information and relationship between attributes in the analyzed dataset. We review different discretization techniques classified according to the proposed taxonomy. The proposed taxonomy emphasizes the advantages and disadvantages of each discretization technique to be able theoretically to find a suitable discretization technique for a particular dataset.