B 6 . A Rough Set Perspective on Data andKnowledge ?

Rough set theory was proposed by Zdziss law Pawlak 24, 25] in the early 1980's. Since then we have witnessed a systematic, worldwide growth of interest in rough set theory and its applications. Rough set theory deals with the analysis of classiicatory properties of data tables. Data represented in the tables can be acquired from measurements or from human experts; although in principle it must be discrete, there exist today methods that allow processing continuous values. The main goal of the rough set analysis is synthesis of approximations of concepts. The most important issues in the synthesis process are: { construction of relevant primitive concepts from which approximations of more complex concepts are assembled, { similarity (closeness) measures between concepts, { construction of operations producing compound concepts from the primitive ones. This presentation shows how several aspects of the above problems are solved by the classical rough set approach and how they are approached by some recent extensions to the classical theory of rough sets. Many of the methods proposed by the rough set approach are also crucial for Knowledge Discovery and Data Mining (KDD) 7].