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Advances in Data Management pp 177–199Cite as

Considerations on Logical Calculi for Dealing with Knowledge in Data Mining

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Part of the Studies in Computational Intelligence book series (SCI,volume 223)

Summary

An attempt to develop and apply logical calculi in exploratory data analysis was made 30 years ago. It resulted in a definition and study of observational logical calculi based on modifications of classical predicate calculi and on mathematical statistics. Additional results followed the definition and first implementations of the GUHA method of mechanizing hypothesis formation. The GUHA method can be seen as one of the first data mining methods. Applications of modern and enhanced implementation of the GUHA method confirmed the generally accepted need to use domain knowledge in the process of data mining. Moreover it inspired considerations on the application of logical calculi for dealing with domain knowledge in data mining. This paper presents these considerations.

Keywords

  • Data Mining
  • Association Rule
  • Data Matrix
  • Domain Knowledge
  • Atomic Consequence

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.

The work described here has been supported by Grant No. 201/08/0802 of the Czech Science Foundation and by Grant No. ME913 of Ministry of Education, Youth and Sports, of the Czech Republic.

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References

  1. Lavrac, N., Dzeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis Horwood, Chichester (1994)

    MATH  Google Scholar 

  2. Aggraval, R., et al.: Fast Discovery of Association Rules. In: Fayyad, U.M., et al. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI Press, Menlo Park (1996)

    Google Scholar 

  3. Hébert, C., Crémille, B.: A Unified View of Objective Interestingness Measures. In: Perner, P. (ed.) MLDM 2007. LNCS, vol. 4571, pp. 533–547. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  4. Geng, L., Hamilton, H.J.: Interestingness Measures for Data Mining: A survey. ACM Computing Surveys 38, 33 (2006)

    CrossRef  Google Scholar 

  5. Hájek, P., Havránek, T.: Mechanising Hypothesis Formation - Mathematical Foundations for a General Theory. Springer, Heidelberg (1978)

    Google Scholar 

  6. Hájek, P. (guest ed.): International Journal of Man-Machine Studies, special issue on GUHA 10 (January 1978)

    Google Scholar 

  7. Hájek, P. (guest ed.): International Journal of Man-Machine Studies, second special issue on GUHA  15 (1981)

    Google Scholar 

  8. Hájek, P., Havránek, T., Chytil, M.: GUHA Method (in Czech). Academia, Prague (1983)

    Google Scholar 

  9. Hájek, P., Sochorová, A., Zvárová, J.: GUHA for personal computers. Computational Statistics & Data Analysis 19, 149–153 (1995)

    MATH  CrossRef  Google Scholar 

  10. Yang, Q., Wu, X.: 10 Challenging Problems in Data Mining Research. International Journal of Information Technology & Decision Making 5(4), 597–604 (2006)

    CrossRef  Google Scholar 

  11. Ralbovský, M., Kuchař, T.: Using Disjunctions in Association Mining. In: Perner, P. (ed.) ICDM 2007. LNCS, vol. 4597, pp. 339–351. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  12. Rauch, J.: Logical Calculi for Knowledge Discovery in Databases. In: Proc. Principles of Data Mining and Knowledge Discovery, Trondheim, Norway, pp. 47–57 (1997)

    Google Scholar 

  13. Rauch, J.: Logic of Association Rules. Applied Intelligence 22, 9–28 (2005)

    MATH  CrossRef  Google Scholar 

  14. Rauch, J.: Definability of Association Rules in Predicate Calculus. In: Lin, T.Y., Ohsuga, S., Liau, C.J., Hu, X. (eds.) Foundations and Novel Approaches in Data Mining, pp. 23–40. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  15. Rauch, J.: Classes of Association Rules - an Overview. In: Lin, T., et al. (eds.) Datamining: Foundations and Practice. Studies in Computational Intelligence, vol. 118, pp. 283–297. Springer, Heidelberg (2008)

    Google Scholar 

  16. Rauch, J., Šimůnek, M.: An Alternative Approach to Mining Association Rules. In: Lin, T.Y., Ohsuga, S., Liau, C.J., Tsumoto, S. (eds.) Data Mining: Foundations, Methods, and Applications, pp. 219–238. Springer, Heidelberg (2005)

    Google Scholar 

  17. Rauch, J., Šimůnek, M.: GUHA Method and Granular Computing. In: Hu, X., et al. (eds.) Proceedings of IEEE conference Granular Computing, pp. 630–635 (2005)

    Google Scholar 

  18. Rauch, J., Šimúnek, M.: Dealing with Background Knowledge in the SEWEBAR Project. In: Berendt, et al. (eds.) Prior Conceptual Knowledge in Machine Learning and Knowledge Discovery. Springer, Heidelberg (2009) (to appear)

    Google Scholar 

  19. Rauch, J.: Logical Aspects of the Measures of Interestingness of Association Rules. In: Koronacki, J., et al. (eds.) Recent Advances in Machine Learning. Springer, Heidelberg (2009) (to appear)

    Google Scholar 

  20. Rauch, J., Šimůnek, M.: Semantic Web Presentation of Analytical Reports from Data Mining - Preliminary Considerations. In: Lin, T.Y., et al. (eds.) Web Intelligence 2007 Proceedings, pp. 3–7 (2007)

    Google Scholar 

  21. Rauch, J., Šimůnek, M.: LAREDAM - Considerations on System of Local Analytical Reports from Data Mining. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds.) Foundations of Intelligent Systems. LNCS (LNAI), vol. 4994, pp. 143–149. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  22. Rauch, J., Tomečková, M.: System of Analytical Questions and Reports on Mining in Health Data – a Case Study. In: Roth, J., et al. (eds.) Proceedings of IADIS European Conference Data Mining 2007, pp. 176–181. IADIS Press (2007)

    Google Scholar 

  23. Šimůnek, M.: Academic KDD Project LISp-Miner. In: Abraham, A., et al. (eds.) Advances in Soft Computing – Intelligent Systems Design and Applications. Springer, Heidelberg (2003)

    Google Scholar 

  24. Svátek, V., Rauch, J., Ralbovský, M.: Ontology-Enhanced Association Mining. In: Ackermann, M., Berendt, B., Grobelnik, M., Hotho, A., Mladenič, D., Semeraro, G., Spiliopoulou, M., Stumme, G., Svátek, V., van Someren, M., et al. (eds.) EWMF 2005 and KDO 2005. LNCS, vol. 4289, pp. 163–179. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Informatics and Statistics, University of Economics, Prague, nám. W. Churchilla 4, 130 67, Prague 3, Czech Republic

    Jan Rauch

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  1. Jan Rauch
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Editor information

Editors and Affiliations

  1. College of Computing and Informatics, University of North Carolina at Charlotte, 28223, Charlotte, N.C., USA

    Zbigniew W. Ras

  2. Wydzial Informatyki, Politechnika Bialostocka, ul.Wiejska 45a, 15-351, Bialystok, Poland

    Agnieszka Dardzinska

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Rauch, J. (2009). Considerations on Logical Calculi for Dealing with Knowledge in Data Mining. In: Ras, Z.W., Dardzinska, A. (eds) Advances in Data Management. Studies in Computational Intelligence, vol 223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02190-9_9

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  • DOI: https://doi.org/10.1007/978-3-642-02190-9_9

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