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Mining Association Rules from Fuzzy DataCubes

Mining Association Rules from Fuzzy DataCubes

Nicolás Marín, Carlos Molina, Daniel Sánchez, M. Amparo Vila
Copyright: © 2010 |Pages: 46
ISBN13: 9781605668581|ISBN10: 1605668583|ISBN13 Softcover: 9781616924478|EISBN13: 9781605668598
DOI: 10.4018/978-1-60566-858-1.ch004
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MLA

Marín, Nicolás, et al. "Mining Association Rules from Fuzzy DataCubes." Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design, edited by Anne Laurent and Marie-Jeanne Lesot, IGI Global, 2010, pp. 84-129. https://doi.org/10.4018/978-1-60566-858-1.ch004

APA

Marín, N., Molina, C., Sánchez, D., & Vila, M. A. (2010). Mining Association Rules from Fuzzy DataCubes. In A. Laurent & M. Lesot (Eds.), Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design (pp. 84-129). IGI Global. https://doi.org/10.4018/978-1-60566-858-1.ch004

Chicago

Marín, Nicolás, et al. "Mining Association Rules from Fuzzy DataCubes." In Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design, edited by Anne Laurent and Marie-Jeanne Lesot, 84-129. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-858-1.ch004

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Abstract

The use of online analytical processing (OLAP) systems as data sources for data mining techniques has been widely studied and has resulted in what is known as online analytical mining (OLAM). As a result of both the use of OLAP technology in new fields of knowledge and the merging of data from different sources, it has become necessary for models to support imprecision. We, therefore, need OLAM methods which are able to deal with this imprecision. Association rules are one of the most used data mining techniques. There are several proposals that enable the extraction of association rules on DataCubes but few of these deal with imprecision in the process and give as result complex rule sets. In this chapter the authors will present a method that manages the imprecision and reduces the complexity. They will study the influence of the use of fuzzy logic using different size problems and comparing the results with a crisp approach.

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