GLOBAL DISCRETIZATION OF CONTINUOUS ATTRIBUTES AS PREPROCESSING FOR MACHINE LEARNING

Real-life data usually are presented in databases by real numbers. On the other hand, most inductive learning methods require a small number of attribute values. Thus it is necessary to convert input data sets with continuous attributes into input data sets with discrete attribut es. Methods of discretization restricted to single continuous attribut es will be called local, while methods that simultaneously convert all continuous attributes will be called global. in this paper, a method of transforming any local discretization method into a global one is p resented. A global discretization method, based on cluster analysis is presented and compared experimentally with three known local methods, transformed into global. Experiments include tenfold cross-validation and leaving-one-out methods for ten real-life data sets. (C) 1996 Els evier Science Inc.