@INPROCEEDINGS { Rsds3267,
author = {Habegger, Benjamin and Quafafou, Mohamed}, 
title = {A Pragmatic Approximation Operator}, 
booktitle = {Proceedings of International Workshop on Rough Set Theory and Granular Computing (RSTGC-2001)}, 
conference = {International Workshop on Rough Set Theory and Granular Computing}, 
series =  = {Bulletin of International Rough Set Society}, 
volume = 5, 
pages = {51-56}, 
month = {May}, 
year = 2001, 
editor = {Hirano, Shoji and Inuiguchi, Masahiro and Tsumoto, Shusaku}, 
issn = {1346-0013}, 
abstract = {Knowledge discovery and data mining often makes use of the Rough Set Theory (RST) to take into account the uncertainty of data sets. Usually two operators building respectively a lower and upper approximation are used. Both the input and output of these operators are sets. In this paper we consider knowledge as a local structure thus giving a more abstract view than usual representations. In this context we introduce a new operator giving a unique pragmatic approximation avoiding the user to choose between the two approximations. This new operator takes as input a set and returns a unique local structure. The approach we take to build this new operator is based on the usual notions of lower and upper approximations of a concept are abstracted using a first operator that translates each approximation into an abstract structures. A second step builds the unique "intermediate" structure consisting in a compromise between the abstract structures associated to the upper and lower approximations. For each of these steps we give a possible application method in the context of inductive logic programming (ILP). We represent an abstract structure as a non oriented graph. A method to orient a structure is also presented allowing to use the obtained structure as a guide to knowledge discovery.}, 
keywords = {rough sets, abstract structure, approximation operator, high level knowledge, inductive logic programming, }, 
}