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An Overview of Mining Fuzzy Association Rules

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Book cover Fuzzy Sets and Their Extensions: Representation, Aggregation and Models

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 220))

Abstract

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Many types of knowledge and technology have been proposed for data mining. Among them, finding association rules from transaction data is most commonly seen. Most studies have shown how binary valued transaction data may be handled. Transaction data in real-world applications, however, usually consist of fuzzy and quantitative values, so designing sophisticated data-mining algorithms able to deal with various types of data presents a challenge to workers in this research field. This chapter thus surveys some fuzzy mining concepts and techniques related to association-rule discovery. The motivation from crisp mining to fuzzy mining will be first described. Some crisp mining techniques for handling quantitative data will then be briefly reviewed. Several fuzzy mining techniques, including mining fuzzy association rules, mining fuzzy generalized association rules, mining both membership functions and fuzzy association rules, will then be described. The advantages and the limitations of fuzzy mining will also be discussed.

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Authors
  1. Tzung-Pei Hong
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  2. Yeong-Chyi Lee
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Editor information

Editors and Affiliations

  1. Department of Automatics and Computation, Universidad Pública de Navarra, Campus Arrosadia s/n, 31006 Pamplona, Spain

    Humberto Bustince

  2. Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada, Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain

    Francisco Herrera

  3. Faculty of Mathematics, Universidad Complutense, Plaza de las Ciencias 3, 28040 Madrid, Spain

    Javier Montero

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Hong, TP., Lee, YC. (2008). An Overview of Mining Fuzzy Association Rules. In: Bustince, H., Herrera, F., Montero, J. (eds) Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Studies in Fuzziness and Soft Computing, vol 220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73723-0_20

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  • DOI: https://doi.org/10.1007/978-3-540-73723-0_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73722-3

  • Online ISBN: 978-3-540-73723-0

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