On conjugate information systems: A proposition on how to learn concepts in humane sciences by means of rough set theory

Abstract

Rough sets, the notion introduced by Zdzislaw Pawlak in early 80's and developed subsequently by many researchers, have proved their usefulness in many problems of Approximate Reasoning, Data Mining, Decision Making. Inducing knowledge from data tables with data in either symbolic or numeric form, rests on computations of dependencies among groups of attributes, and it is a well-developed part of the rough set theory. Recently, some works have been devoted to problems of concept learning in humane sciences via rough sets. This problem is distinct as to its nature from learning from data, as it does involve a dialogue between the teacher and the pupil in order to explain the meaning of a concept whose meaning is subjective, vague and often initially obscure, through a series of interchanges, corrections of inappropriate choices, explanations of reasons for corrections, finally reaching a point, where the pupil has mastered enough knowledge of the subject to be able in future to solve related problems fairly satisfactorily. We propose here an approach to the problem of learning concepts in humane sciences based on the notion of a conjugate system; it is a family of information systems, organized by means of certain requirements in order to allow a group of students and a teacher to analyze a common universe ofobjects and to correct faulty choices of attribute value in order to reach a more correct understanding of the concept. © Springer-Verlag Berlin Heidelberg 2007.