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Methods and Supporting Technologies for Data Analysis pp 71–113Cite as

Applications of Fuzzy and Rough Set Theory in Data Mining

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 225))

Abstract

The explosion of very large databases has created extraordinary opportunities for monitoring, analyzing and predicting global economical, geographical, demographic, medical, political, and other processes in the world. Statistical analysis and data mining techniques have emerged for these purposes. Data mining is the process of discovering previously unknown but potentially useful patterns, rules, or associations from huge quantity of data. Data mining can be performed on different data repositories such as relational databases, data warehouses, transactional databases, sequence databases, spatial databases, spatio-temporal databases, and text databases, etc. Typically, data mining functionalities can be classified into two categories: descriptive and predictive. Descriptive mining tasks aim at characterizing the general properties of the data in the databases, while predictive mining tasks perform inherence on the current data in order to make prediction in future.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Northern Arizona University, Flagstaff, AZ 86011-5600

    Dan Li

  2. Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588-0115

    Jitender S. Deogun

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  1. Dan Li
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  2. Jitender S. Deogun
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Editors and Affiliations

  1. Institute of Computer Science, Technical University of Lodz , Wolczanska 215, 90-924, Lodz, Poland

    Danuta Zakrzewska  & Liliana Byczkowska-Lipinska  & 

  2. Facultad de Informatica, Universidad Politecnica de Madrid , Campus de Montegancedo s/n, 28660, Boadilla del Monte Madrid, Spain

    Ernestina Menasalvas

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Li, D., Deogun, J.S. (2009). Applications of Fuzzy and Rough Set Theory in Data Mining. In: Zakrzewska, D., Menasalvas, E., Byczkowska-Lipinska, L. (eds) Methods and Supporting Technologies for Data Analysis. Studies in Computational Intelligence, vol 225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02196-1_4

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  • DOI: https://doi.org/10.1007/978-3-642-02196-1_4

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  • Online ISBN: 978-3-642-02196-1

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