@ARTICLE { Rsds2420,
author = {Pawlak, Zdzislaw}, 
title = {Some Issues on Rough Sets}, 
booktitle = {T. Rough Sets}, 
series =  = {Lecture Notes in Computer Science}, 
journal = {Transactions on Rough Sets}, 
number = 	{(no. I)}, 
volume = 3100, 
pages = {1-58}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
year = 2004, 
editor = {Peters, James F. and Skowron, Andrzej and Grzymala-Busse, Jerzy W. and Kostek, Bozena and Swiniarski, Roman W. and Szczuka, Marcin}, 
url = {http://www.springer.de}, 
issn = {0302-9743}, 
isbn = {3-540-22374-6}, 
abstract = {The aim of this paper is to give rudiments of rough set theory and present some recent research directions proposed by the author. Rough set theory is a new mathematical approach to imperfect knowledge. The problem of imperfect knowledge has been tackled for a long time by philosophers, logicians and mathematicians. Recently it became also a crucial issue for computer scientists, particularly in the area of artificial intelligence. There are many approaches to the problem of how to understand and manipulate imperfect knowledge. The most successful one is, no doubt, the fuzzy set theory proposed by Lotfi Zadeh [1]. Rough set theory proposed by the author in [2] presents still another attempt to this problem. This theory has attracted attention of many researchers and practitioners all over the world, who have contributed essentially to its development and applications. Rough set theory overlaps with many other theories. However we will refrain to discuss these connections here. Despite this, rough set theory may be considered as an independent discipline in its own right. Rough set theory has found many interesting applications. The rough set approach seems to be of fundamental importance to AI and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, inductive reasoning and pattern recognition.}, 
keywords = {rough sets, fuzzy sets, approximate reasoning, information system, decision systems, reducts, decision algorithms, conflict analysis, data analysis, flow graphs, decision networks, }, 
}