@ARTICLE { Rsds3583,
author = {Chan, Chien-Chung}, 
title = {A rough set approach to attribute generalization in data mining}, 
journal = {Information Sciences: An International Journal}, 
number = 	{(no. 1-4)}, 
volume = 107, 
pages = {169-176}, 
publisher = {Elsevier Science Inc.}, 
address = {Amsterdam, The Netherlands}, 
month = {June}, 
year = 1998, 
issn = {0020-0255}, 
abstract = {This paper presents a method for updating approximations of a concept incrementally. The results can be used to implement a quasi-incremental algorithm for learning classification rules from very large data bases generalized by dynamic conceptual hierarchies provided by users. In general, the process of attribute generalization may introduce inconsistency into a generalized relation. This issue is resolved by using the inductive learning algorithm, LERS based on rough set theory.}, 
keywords = {rough sets, data mining, inductive learning, }, 
}