Fuzzy Rules in Data Mining: From Fuzzy Associations to Gradual Dependencies

  • Eyke Hüllermeier
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 271)


Fuzzy rules, doubtlessly one of the most powerful tools of fuzzy logic, have not only been used successfully in established application areas like control engineering and approximate reasoning, but more recently also in the field of data mining. In this chapter, we provide a synthesis of different approaches to fuzzy association analysis, that is, the data-driven extraction of interesting patterns expressed in the form of fuzzy rules. In this regard, we highlight a specific advantage of a fuzzy in comparison to a conventional approach, namely an increased expressiveness that allows for representing patterns of interest in a more distinctive way. Therefore, we specifically focus on the modeling of a less common type of pattern, namely gradual dependencies between attributes in a data set.


Data Mining Fuzzy Logic Association Rule Fuzzy Rule Association Rule Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of MarburgMarburgGermany

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