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Towards Advanced Data Analysis by Combining Soft Computing and Statistics pp 291–303Cite as

Derivation of Linguistic Summaries is Inherently Difficult: Can Association Rule Mining Help?

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 285))

Abstract

We present first the essence of fuzzy linguistic summaries, indicate their relation to fuzzy queries with linguistic quantifiers, and show a taxonomy of protoforms of linguistic summaries indicating that a general protoform, which corresponds to some type of an IF-THEN rule, parallels the structure and form of an association rule. We show that the use of our fuzzy querying interface makes it possible to operationalize the process of definition, updating and processing of fuzzy terms in linguistic data summaries (fuzzy values, fuzzy relations, fuzzy linguistic quantifiers, etc.) and their corresponding fuzzy association rules of a special type. We develop for them a mining algorithm based on AprioriTID. This is clearly a step towards an effective and efficient method for the generation of linguistic data summaries which is badly needed for their proliferation in practice.

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Author information

Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk & Sławomir Zadrożny

  2. Technical University of Radom, ul. Malczewskiego 29, 26-600, Radom, Poland

    Sławomir Zadrożny

Authors
  1. Janusz Kacprzyk
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  2. Sławomir Zadrożny
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Corresponding author

Correspondence to Janusz Kacprzyk .

Editor information

Editors and Affiliations

  1. Intelligent Data Analysis & Graphical, Models Research Unit, European Centre for Soft Computing, C/ Gonzalo Gutierre, Mieres, 33600, Asturias, Spain

    Christian Borgelt

  2. , Departamento de Estadistica e I. O. y, Universidad de Oviedo, C/Calvo Sotelo, s/n, Oviedo, 33071, Spain

    María Ángeles Gil

  3. Instituto Superior Técnico, Department of Mechanical Engineering, Technical University Lisbon, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal

    João M.C. Sousa

  4. Labo. Microelectronique, Université Catholique de Louvain, place du Levant 3, Leuven, 1348, Belgium

    Michel Verleysen

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Kacprzyk, J., Zadrożny, S. (2013). Derivation of Linguistic Summaries is Inherently Difficult: Can Association Rule Mining Help?. In: Borgelt, C., Gil, M., Sousa, J., Verleysen, M. (eds) Towards Advanced Data Analysis by Combining Soft Computing and Statistics. Studies in Fuzziness and Soft Computing, vol 285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30278-7_23

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  • DOI: https://doi.org/10.1007/978-3-642-30278-7_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30277-0

  • Online ISBN: 978-3-642-30278-7

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