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Discovering Knowledge With The Rough Set Approach

Author

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  • Jiøí Mazurek

    (Sileasian University in Opava, School of Business Administration in Karviná, Department of Mathematical Methods in Economics)

Abstract

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

Suggested Citation

  • Jiøí Mazurek, 2013. "Discovering Knowledge With The Rough Set Approach," Polish Journal of Management Studies, Czestochowa Technical University, Department of Management, vol. 7(1), pages 245-254, June.
    • Handle: RePEc:pcz:journl:v:7:y:2013:i:1:p:245-254
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    References listed on IDEAS

    as
    1. Greco, Salvatore & Matarazzo, Benedetto & Slowinski, Roman, 2001. "Rough sets theory for multicriteria decision analysis," European Journal of Operational Research, Elsevier, vol. 129(1), pages 1-47, February.
    2. Jiøí Mazurek, 2012. "The Evaluation Of Conflicts’ Degree In Group Decision Making," Polish Journal of Management Studies, Czestochowa Technical University, Department of Management, vol. 5(1), pages 107-119, June.
    3. Tay, Francis E. H. & Shen, Lixiang, 2002. "Economic and financial prediction using rough sets model," European Journal of Operational Research, Elsevier, vol. 141(3), pages 641-659, September.
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    Cited by:

    1. Loureiro, Sandra Maria Correia & Guerreiro, João & Tussyadiah, Iis, 2021. "Artificial intelligence in business: State of the art and future research agenda," Journal of Business Research, Elsevier, vol. 129(C), pages 911-926.

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    More about this item

    Keywords

    information system; knowledge discovery; rough sets; rule extraction; uncertainty;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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