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Mining Association Rules Using Fuzzy Inference on Web Data

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Advances in Web Intelligence (AWIC 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2663))

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Abstract

The association rules model is one of most widely used models in data mining. An association rule is an implication of the form X → Y, where X and Y are a set of items that satisfy two constraints, given by the user, called minimum support (minsup) and minimum confidence (minconf). Normally, the values of minsup and minconf are crisp. In this paper, we analyze how association rules mining is affected when these values are treated as fuzzy.

In order to calculate frequent itemsets and to generate association rules, an algorithm based on fuzzy sets is proposed. Using the fuzzy inference system, FUZZYC, the algorithm offers to user an intuitive way for defining and tuning the minconf and minsup parameters.

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Author information

Authors and Affiliations

  1. Escuela de Ingeniería de Sistemas y Computación, Universidad del Valle, Cali, Colombia

    Mariluz Martínez, Gelver Vargas & Marta Millán

  2. Carrera de Ingeniería de Sistemas y Computación, Pontificia Universidad Javeriana, Cali, Colombia

    Andrés Dorado

Authors
  1. Mariluz Martínez
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  2. Gelver Vargas
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  3. Andrés Dorado
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  4. Marta Millán
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Editor information

Editors and Affiliations

  1. Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo, Boadilla del Monte, 28660, Madrid, Spain

    Ernestina Menasalvas  & Javier Segovia  & 

  2. Institute of Computer Science, Technical University of Lodz, ul.Sterlinga 16/18, 90-217, Lodz, Poland

    Piotr S. Szczepaniak

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© 2003 Springer-Verlag Berlin Heidelberg

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Martínez, M., Vargas, G., Dorado, A., Millán, M. (2003). Mining Association Rules Using Fuzzy Inference on Web Data. In: Menasalvas, E., Segovia, J., Szczepaniak, P.S. (eds) Advances in Web Intelligence. AWIC 2003. Lecture Notes in Computer Science, vol 2663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44831-4_9

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  • DOI: https://doi.org/10.1007/3-540-44831-4_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40124-7

  • Online ISBN: 978-3-540-44831-0

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