DISCOVERING KNOWLEDGE WITH THE ROUGH SET APPROACH

The rough set theory, which originated in the early 1980s, provides an alternative approach to the fuzzy set theory, when dealing with uncertainty, vagueness or inconsistence often encountered in real-world situations. The fundamental premise of the rough set theory is that every object of the universe is associated with some information, which is frequently imprecise and insufficient to distinguish among objects. In the rough set theory, this information about objects is represented by an information system (decision table). From an information system many useful facts and decision rules can be extracted, which is referred as knowledge discovery, and it is successfully applied in many fields including data mining, artificial intelligence learning or financial investment. The aim of the article is to show how hidden knowledge in the real-world data can be discovered within the rough set theory framework. After a brief preview of the rough set theory’s basic concepts, knowledge discovery is demonstrated on an example of baby car seats evaluation. For a decision rule extraction, the procedure of Ziarko and Shan is used