@INPROCEEDINGS { Rsds4509,
author = {Tsay, Li-Shiang and Ras, Zbigniew W. and Im, Seunghyun}, 
title = {Multi-granularity Classification Rule Discovery Using ERID}, 
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 = {491--499}, 
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 = {This paper introduces the use of ERID [1] algorithm for classification rule discovery at various levels of granularity. We use an incomplete information system and attribute value hierarchy to extract rules. The incomplete information system is capable of storing weighted attribute values and the domains of those attributes are organized using a hierarchical tree structure. The granularity of attribute values can be adjusted using the attribute value hierarchy. The result is then processed through ERID, which is designed to discover rules from partially incomplete information systems. The capability of handling incomplete data enables to build more specific and general classification rules.}, 
keywords = {knowledge discovery, incomplete information system, attribute hierarchy, rough sets, }, 
}