DISCOVERING KNOWLEDGE WITH THE ROUGH SET APPROACH
Polish Journal of Management Studies, 2013, vol. 7, issue 1, 245-254
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
Keywords: information system; knowledge discovery; rough sets; rule extraction; uncertainty (search for similar items in EconPapers)
JEL-codes: D83 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://www.pjms.zim.pcz.pl/PDF/PJMS7/DISCOVERING%2 ... 20SET%20APPROACH.pdf (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:pcz:journl:v:7:y:2013:i:1:p:245-254
Access Statistics for this article
More articles in Polish Journal of Management Studies from Czestochowa Technical University, Department of Management Contact information at EDIRC.
Bibliographic data for series maintained by Paula Bajdor ().