Abstract
In the data-mining field, many learning methods — such as association rules, Bayesian networks, and induction rules (Grzymala-Busse & Stefanowski, 2001) — can handle only discrete attributes. Therefore, before the machine-learning process, it is necessary to re-encode each continuous attribute in a discrete attribute constituted by a set of intervals. For example, the age attribute can be transformed in two discrete values representing two intervals: less than 18 (a minor) and 18 or greater. This process, known as discretization, is an essential task of the data preprocessing not only because some learning methods do not handle continuous attributes, but also for other important reasons. The data transformed in a set of intervals are more cognitively relevant for a human interpretation (Liu, Hussain, Tan, & Dash, 2002); the computation process goes faster with a reduced level of data, particularly when some attributes are suppressed from the representation space of the learning problem if it is impossible to find a relevant cut (Mittal & Cheong, 2002); the discretization can provide nonlinear relations — for example, the infants and the elderly people are more sensitive to illness. This relation between age and illness is then not linear — which is why many authors propose to discretize the data even if the learning method can handle continuous attributes (Frank & Witten, 1999). Lastly, discretization can harmonize the nature of the data if it is heterogeneous — for example, in text categorization, the attributes are a mix of numerical values and occurrence terms (Macskassy, Hirsh, Banerjee, & Dayanik, 2001).