Rough sets for data mining and knowledge discovery

Abstract

The problem of handling imperfect and approximate knowledge has been recently recognized as the crucial issue in solving several complex real-life problems, also in the case of data mining and knowledge discovery. Among many approaches to imperfect knowledge, such as fuzzy sets of Zadeh (1965) various forms of neural networks and evidence theory, to name a few, the theory of information systems and rough sets introduced by Z. Pawlak in 1982 has lately gain substantial attention. Rough sets are attractive from the computational point of view because the underlying concept of an information system is basically a relation in a database. Rough sets also have a rather intuitive common-sense interpretation (i.e. belief and plausibility) having at the same time a sound mathematical definition (i.e. lower and upper bounds)