@ARTICLE { Rsds4694,
author = {Lasek, Piotr and Kryszkiewicz, Marzena}, 
title = {FUN: Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute}, 
journal = {Transactions on Rough Sets}, 
number = 	{(no. IX)}, 
volume = 5390, 
pages = {76--95}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
year = 2008, 
issn = {0302-9743}, 
isbn = {978-3-540-89875-7}, 
abstract = {n this paper, we present our Fun algorithm for discovering minimal sets of conditional attributes functionally determining a given dependent attribute. In particular, the algorithm is capable of discovering Rough Sets certain, generalized decision, and membership distribution reducts. Fun can operate either on partitions of objects or alternatively on stripped partitions, which do not store singleton groups. It is capable of using functional dependencies occurring among conditional attributes for pruning candidate dependencies. In this paper, we offer further reduction of stripped partitions, which allows correct determination of minimal functional dependencies provided optional candidate pruning is not carried out. In the paper we consider six variants of Fun, including two new variants using reduced stripped partitions. We have carried out a number of experiments on benchmark data sets to test the efficiency of all variants of Fun. We have also tested the efficiency of the Fun's variants against the Rosetta and RSES toolkits' algorithms computing all reducts and against Tane, which is one of the most efficient algorithms computing all minimal functional dependencies. The experiments prove that Fun is up to 3 orders of magnitude faster than the the Rosetta and RSES toolkits' algorithms and faster than Tane up to 30 times.}, 
keywords = {rough sets, information system, decision table, reduct, functional dependency, }, 
}