@INPROCEEDINGS { Rsds3512,
author = {Cercone, Nick and Ziarko, Wojciech and Hamilton, Howard J. and Shan, Ning}, 
title = {Discretization of Continuous Valued Attributes in Classification Systems}, 
booktitle = {Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery}, 
conference = {International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery}, 
pages = {74--81}, 
publisher = {Japanese Research Group on Rough Sets}, 
address = {Tokyo, Japan}, 
month = {November}, 
year = 1996, 
editor = {Tsumoto, Shusaku and Kobayashi, Satoshi and Yokomori, Takashi and Tanaka, Hiroshi and Nakamura, Akira}, 
isbn = {4-947717-01-7}, 
abstract = {Discretization methods that transform continuous values to discrete ones for learning systems are described and empirically evaluated. Three novel discretization methods, based on attribute reduction in rough sets, are introduced. Seven techniques for selecting attributes for removal or merging show that none of the methods is clearly best.}, 
keywords = {discretizations, machine learning, attribute selection, rough sets, }, 
}