@INPROCEEDINGS { Rsds4476,
author = {Blajdo, Piotr and Busse-Grzymala, Jerzy W. and Hippe, Zdzislaw S. and Knap, Maksymilian and Mroczek, Teresa and Piatek, Lukasz}, 
title = {A Comparison of Six Approaches to Discretization - A Rough Set Perspective}, 
booktitle = {Proceedings of the Third International Conference on Rough Sets and Knowledge Technology (RSKT'2008)}, 
conference = {Rough Sets and Knowledge Technology}, 
series =  = {Lecture Notes in Artificial Intelligence}, 
volume = 5009, 
pages = {31--38}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
month = {May}, 
year = 2008, 
editor = {Wang, Guoying and Li, Tianrui and Grzymala-Busse, Jerzy W. and Miao, Duoqian and Skowron, Andrzej and Yao, Yiyu}, 
issn = {03029743}, 
isbn = {978-3-540-69069-6}, 
abstract = {We present results of extensive experiments performed on nine data sets with numerical attributes using six promising discretization methods. For every method and every data set 30 experiments of ten-fold cross validation were conducted and then means and sample standard deviations were computed. Our results show that for a specific data set it is essential to choose an appropriate discretization method since performance of discretization methods differ significantly. However, in general, among all of these discretization methods there is no statistically significant worst or best method. Thus, in practice, for a given data set the best discretization method should be selected individually.}, 
keywords = {rough sets, discretization, cluster analysis, merging intervals, ten-fold cross validation, test on the difference between means, f-test, }, 
}