@INPROCEEDINGS { Rsds2717,
author = {Nguyen, Hung Son}, 
title = {Scalable Classification Method Based on Rough Sets}, 
booktitle = {Rough Sets and Current Trends in Computing}, 
conference = {Rough Sets and Current Trends in Computing}, 
series =  = {Lecture Notes in Arificial Intelligence}, 
volume = 2475, 
pages = {433--440}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
month = {October}, 
year = 2002, 
editor = {Alpigini, James J. and Peters, James F. and Skowron, Andrzej and Zhong, Ning Liu Jiming}, 
issn = {0302-9743}, 
isbn = {3-540-44274-X}, 
abstract = {The existing rough set based methods are not applicable for large data set because of the high time and space complexity and the lack of scalability. We present a classification method, which is equivalent to rough set based classification methods, but is scalable and applicable for large data sets. The proposed method is based on lazy learning idea [2] and Apriori algorithm for sequent item-set approaches [1]. In this method the set of decision rules matching the new object is generated directly from training set. Accept classification task, this method can be used for adaptive rule generation system where data is growing up in time.}, 
keywords = {data mining, scalability, rough set, lazy learning, }, 
}