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Granular Computing Based Data Mining in the Views of Rough Set and Fuzzy Set

Granular Computing Based Data Mining in the Views of Rough Set and Fuzzy Set

Guoyin Wang, Jun Hu, Qinghua Zhang, Xianquan Liu, Jiaqing Zhou
Copyright: © 2010 |Pages: 31
ISBN13: 9781605663241|ISBN10: 1605663247|ISBN13 Softcover: 9781616923037|EISBN13: 9781605663258
DOI: 10.4018/978-1-60566-324-1.ch007
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MLA

Wang, Guoyin, et al. "Granular Computing Based Data Mining in the Views of Rough Set and Fuzzy Set." Novel Developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation, edited by JingTao Yao, IGI Global, 2010, pp. 148-178. https://doi.org/10.4018/978-1-60566-324-1.ch007

APA

Wang, G., Hu, J., Zhang, Q., Liu, X., & Zhou, J. (2010). Granular Computing Based Data Mining in the Views of Rough Set and Fuzzy Set. In J. Yao (Ed.), Novel Developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation (pp. 148-178). IGI Global. https://doi.org/10.4018/978-1-60566-324-1.ch007

Chicago

Wang, Guoyin, et al. "Granular Computing Based Data Mining in the Views of Rough Set and Fuzzy Set." In Novel Developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation, edited by JingTao Yao, 148-178. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-324-1.ch007

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Abstract

Granular computing (GrC) is a label of theories, methodologies, techniques, and tools that make use of granules in the process of problem solving. The philosophy of granular computing has appeared in many fields, and it is likely playing a more and more important role in data mining. Rough set theory and fuzzy set theory, as two very important paradigms of granular computing, are often used to process vague information in data mining. In this chapter, based on the opinion of data is also a format for knowledge representation, a new understanding for data mining, domain-oriented data-driven data mining (3DM), is introduced at first. Its key idea is that data mining is a process of knowledge transformation. Then, the relationship of 3DM and GrC, especially from the view of rough set and fuzzy set, is discussed. Finally, some examples are used to illustrate how to solve real problems in data mining using granular computing. Combining rough set theory and fuzzy set theory, a flexible way for processing incomplete information systems is introduced firstly. Then, the uncertainty measure of covering based rough set is studied by converting a covering into a partition using an equivalence domain relation. Thirdly, a high efficient attribute reduction algorithm is developed by translating set operation of granules into logical operation of bit strings with bitmap technology. Finally, two rule generation algorithms are introduced, and experiment results show that the rule sets generated by these two algorithms are simpler than other similar algorithms.

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