@INPROCEEDINGS { Rsds3831,
author = {Chmielewski, Michal R. and Busse-Grzymala, Jerzy W.}, 
title = {Global Discretization of Continuous Attributes as Preprocessing for Machine Learning}, 
booktitle = {Soft Computing}, 
conference = {International Workshop on Rough Sets and Soft Computing}, 
pages = {294--297}, 
publisher = {The Society for Computer Simulation}, 
address = {San Diego, U.S.A.}, 
month = {November}, 
year = 1994, 
editor = {Lin, Tsau Young and Wildberger, A.M.}, 
isbn = {1-56555-077-3}, 
abstract = {Real-life data usually are presented in databases by real numbers. On the other hand, most inductive learning methods require small number of attribute values. Thus it is necessary to convert input data sets with continuous attributes into input data sets with discrete attributes. Methods of discretization restricted to single continuous attributes 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 presented. A global discretization method, based on cluster analysis, is presented and compared experimentally with three known local methods, transformed into global. Experiments include ten-fold cross validation and learning-one-out methods for ten real-life data sets.}, 
keywords = {machine learning, discretization of continuous attributes, cluster analysis, rough set theory (RST), }, 
}