@ARTICLE { Rsds3696,
author = {Busse-Grzymala, Jerzy W. and Chmielewski, Michal R.}, 
title = {Global discretization of continuous attributes as preprocessing for machine learning}, 
journal = {International Journal of Approximate Reasoning}, 
number = 	{(no. 4)}, 
volume = 15, 
pages = {319-331}, 
publisher = {Elsevier Science Inc.}, 
address = {Amsterdam, The Netherlands}, 
month = {November}, 
year = 1996, 
url = {http://www.elsevier.com}, 
issn = {0888-613X}, 
abstract = {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 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 tenfold cross-validation and leaving-one-out methods for ten real-life data sets.}, 
keywords = {discretization, quantization, continuous attributes, machine learning from examples, rough set theory (RST), }, 
}