@INPROCEEDINGS { Rsds2952,
author = {Nguyen, Hung Son}, 
title = {Approximate Boolean Reasoning Approach to Rough Sets and Data Mining}, 
booktitle = {Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, Part II}, 
series =  = {Lecture Notes in Artificial Intelligence}, 
volume = 3642, 
pages = {12--22}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
month = {August}, 
year = 2005, 
editor = {Slezak, Dominik and Yao, Jing Tao and Peters, James F. and Ziarko, Wojciech and Hu, Xiaohua}, 
issn = {0302-9743}, 
isbn = {3-540-28660-8}, 
abstract = {Many problems in rough set theory have been successfully solved by boolean reasoning (BR) approach. The disadvantage of this elegant methodology is based on its high space and time complexity. In this paper we present a modified BR approach that can overcome those difficulties. This methodology is called the approximate boolean reasoning (ABR) approach. We summarize some most recent applications of ABR approach in development of new efficient algorithms in rough sets and data mining.}, 
keywords = {rough sets, data mining, boolean reasoning, }, 
}