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Implementation of Background Knowledge and Properties Induced by Fuzzy Confirmation Measures in Apriori Algorithm

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International Joint Conference CISIS’12-ICEUTE´12-SOCO´12 Special Sessions

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 189))

Abstract

This work contributes to so-called association analysis. Its goal is to search for dependencies (called associations) between attributes in large scale data sets. Recently the authors theoretically studied some properties of fuzzy confirmation measures and possible application of background (resp. expert) knowledge into associations mining process. In this work we implement our recent results into well-known Apriori algorithm. Despite of the fact that the presented algorithm allows us to mine linguistic associations, i.e., associations interpretable in natural language, basic ideas of this algorithm can be easily extended to less specific model of fuzzy sets.

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References

  1. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules, vol. 1215, pp. 487–499. Citeseer (1994)

    Google Scholar 

  2. Dubois, D., Hüllermeier, E., Prade, H.: A systematic approach to the assessment of fuzzy association rules. Data Mining and Knowledge Discovery 13, 167–192 (2006)

    Article  MathSciNet  Google Scholar 

  3. Fu, H.: Cluster analysis and association analysis for the same data, pp. 576–581. University of Cambridge, UK (2008)

    Google Scholar 

  4. Hájek, P.: The question of a general concept of the guha method. Kybernetika, 505–515 (1968)

    Google Scholar 

  5. Hájek, P., Havránek, T.: Mechanizing hypothesis formation. Mathematical foundations for a general theory. Springer, Heidelberg (1978)

    Book  MATH  Google Scholar 

  6. Kupka, J., Tomanová, I.: Some extensions of mining of linguistic associations. Neural Network World 20, 27–44 (2010)

    Google Scholar 

  7. Kupka, J., Tomanová, I.: Some dependencies among attributes given by fuzzy confirmation measures. In: Proc. of the LFA-EUSFLAT 2011, France, pp. 498–505 (2011)

    Google Scholar 

  8. Kupka, J., Tomanová, I.: Dependencies among attributes given by fuzzy confirmation measures. Expert Systems with Applications 39(9), 7591–7599 (2012)

    Article  Google Scholar 

  9. Novák, V., Perfilieva, I., Dvořák, A., Che, Q., Wei, Q., Yan, P.: Mining pure linguistic associations from numerical data. International Journal of Approximate Reasoning 48(1), 4–22 (2008)

    Article  MATH  Google Scholar 

  10. Novák, V., Perfilieva, I., Močkoř, J.: Mathematical principles of fuzzy logic. Kluwer Academic Publishers, Boston (1999)

    Book  MATH  Google Scholar 

  11. Rauch, J.: Logic of association rules. Applied Intelligence 22, 9–28 (2005)

    Article  MATH  Google Scholar 

  12. Tsay, Y.J., Chang-Chien, Y.W.: An efficient cluster and decomposition algorithm for mining association rules. Information Sciences 160, 161–171 (2004)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute for Research and Applications of Fuzzy Modeling, Centre of Excellence IT4Innovations – Division of the University of Ostrava, 30. dubna 22, Ostrava, Czech Republic

    Iva Tomanová & Jiří Kupka

Authors
  1. Iva Tomanová
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  2. Jiří Kupka
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Corresponding author

Correspondence to Iva Tomanová .

Editor information

Editors and Affiliations

  1. , Department of Civil Engineering, University of Burgos, Campus Vena (Edif.C), C/ Francisco de Vitoria, s/n, Burgos, 09006, Spain

    Álvaro Herrero

  2. VŠB-TU Ostrava, 17. listopadu 15, Ostrava, 70833, Czech Republic

    Václav Snášel

  3. MIR Labs, Scientific Network for Innovation, Machine Intelligence Research Labs, Auburn, 98071, USA

    Ajith Abraham

  4. VŠB-TU Ostrava, 17. listopadu 15, Ostrava, 70833, Czech Republic

    Ivan Zelinka

  5. , Department of Civil Engineering, University of Burgos, Campus Vena (Edif.C), C/ Francisco de Vitoria, s/n, Burgos, 09006, Spain

    Bruno Baruque

  6. Universidad de Salamanca, Plaza de la Merced S/N, Salamanca, 37008, Spain

    Héctor Quintián

  7. University of Coruña, Avda. 19 de febrero, s/n, Coruña, 15405 A, Spain

    José Luis Calvo

  8. y León, Pol. Ind. Villalonquéjar, Instituto Tecnológico de Castilla, Lopez Bravo 70, Burgos, 09001, Spain

    Javier Sedano

  9. Universidad de Salamanca, Plaza de la Merced S/N, Salamanca, 37008, Spain

    Emilio Corchado

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Tomanová, I., Kupka, J. (2013). Implementation of Background Knowledge and Properties Induced by Fuzzy Confirmation Measures in Apriori Algorithm. In: Herrero, Á., et al. International Joint Conference CISIS’12-ICEUTE´12-SOCO´12 Special Sessions. Advances in Intelligent Systems and Computing, vol 189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33018-6_55

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  • DOI: https://doi.org/10.1007/978-3-642-33018-6_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33017-9

  • Online ISBN: 978-3-642-33018-6

  • eBook Packages: EngineeringEngineering (R0)

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