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The GUHA Method, Data Preprocessing and Mining

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Database Support for Data Mining Applications

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2682))

Abstract

The paper surveys basic principles and foundations of the GUHA method, relation to some well-known data mining systems, main publications, existing implementations and future plans.

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References

  1. Agrawal, R., Manilla, H., Sukent, R., Toivonen, A., Verkamo, A.: Fast discovery of Association rules. In: Advance in Knowledge Discovery and Data Mining, pp. 307–328. AAAI, Menlo Park (1996)

    Google Scholar 

  2. Coufal, D., Holeňa, M., Sochorová, A.: Coping with Discovery Challenge by GUHA. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 7–16. Springer, Heidelberg (1999)

    Google Scholar 

  3. Coufal, D.: GUHA Analysis of Air Pollution Data. In: Kurková, V., Steele, N.C., Neruda, R., Kárný, M. (eds.) Artificial Neural Nets and Genetic Algorithms. Proceedings of the International conference ICANNGA 2001, Prague, pp. 465–468. Springer, Wien (2001)

    Google Scholar 

  4. Dolejší, P., Lín, V., Rauch, J., Šebek, M.: System of KDD Tasks and Results within the STULONG Project. In: Berka (ed.) Discovery Challenge Workshop Notes, University Helsinki (2002)

    Google Scholar 

  5. Džeroski, S., Lavrač, N. (eds.): Relational Data Mining, p. 398. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  6. Feglar, T.: The GUHA architecture. In: Proc. Relmics, Tilburg (The Netherlands), vol. 6, pp. 358–364.

    Google Scholar 

  7. Feglar, T.: The GUHA Virtual Machine - Frameworks and Key Concept - Frameworks and Key Concept, Research Report COST 274 (2001)

    Google Scholar 

  8. Feglar, T.: Modeling of a Engine Based Approach to the Decision Support. In: Proceedings of ADBIS 2002 Conference, Research Communications, vol. 2, Bratislava, September 2002, pp. 98–107 (2002)

    Google Scholar 

  9. Hájek, P.: On generalized quantifiers, finite sets and data mining. In: Accepted for presentation on the conference IIS 2003 (Intelligent Information Systems), Zakopane, Poland (June 2–5, 2003)

    Google Scholar 

  10. Hájek, P.: Metamathematics of Fuzzy Logic. Kluwer, Dordrecht (1998)

    Book  MATH  Google Scholar 

  11. Hájek, P.: Relations in GUHA-style data mining. In: de Swart, H. (ed.) RelMiCS 2001. LNCS, vol. 2561, p. 81. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  12. Hájek, P.: The GUHA method and mining association rules. In: Proc. CIMA 2001, Bangor, Wales, pp. 533–539 (2001)

    Google Scholar 

  13. Hájek, P.: The new version of the GUHA procedure ASSOC. In: COMPSTAT 1984, pp. 360–365 (1984)

    Google Scholar 

  14. Hájek, P., Havel, I., Chytil, M.: The GUHA method of automatic hypotheses determination. Computing 1, 293–308 (1966)

    Article  MATH  Google Scholar 

  15. Hájek, P., Havránek, T.: Mechanizing Hypothesis Formation (Mathematical Foundations for a General Theory), p. 396. Springer, Heidelberg (1978)

    Book  MATH  Google Scholar 

  16. Hájek, P., Havránek, T.: Mechanizing Hypothesis Formation (Mathematical Foundations for a General Theory), Internet edn., http://www.cs.cas.cz/~hajek/guhabook/

  17. Hájek, P., Holeňa, M.: Formal logics of discovery and hypothesis formation by machine. Theoretical Computer Science (to appear)

    Google Scholar 

  18. Hájek, P.(guest ed.): International Journal of Man-Machine Studies, 10(1) (special issue on GUHA). Introductory paper of the volume is Hájek, Havránek: The GUHA method - its aims and techniques. Int. J. Man-Machine Studies 10, 3–22 (1977)

    Google Scholar 

  19. Hájek, P.(guest ed.): International Journal for Man-Machine Studies.  15(3) (second special issue on GUHA)

    Google Scholar 

  20. Hájek, P., Sochorová, A., Zvárová, J.: GUHA for personal computers. Comp. Stat., Data Arch. 19, 149–153

    Google Scholar 

  21. Hálová, J., Feglar, T.: Systematic approach to the choice of optimum variant of radioactive waste management. In: Proceedings of 6th ISAHP 2001 Conference, Berne, Switzeland, August 2001, pp. 139–144 (2001)

    Google Scholar 

  22. Hálová, J., Žák, P.: Coping Discovery challenge of mutagenes discovery with GUHA+/− for windows. In: The Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining. Workshop KDD Challenge 2000. International Workshop on KDD Challenge on Real-world Data, Kyoto, pp. 55–60 (2000); Pacific-Asia Conference on Knowledge Discovery and Data Mining /4./, Kyoto, Japan

    Google Scholar 

  23. Hálová, J., Žák, P.: Drug Tailoring by GUHA +/− for Windows. In: Challenges for MCDM in the New Millenium, Ankara, Middle East Technical University, p. 50 (2000), International Conference MCDM /15./, Ankara, Turkey

    Google Scholar 

  24. Hálová, J., Žák, P.: Fingerprint Descriptors in Tailoring New Drugs Using GUHA Method. In: 51th Meeting of the European Working Group Multicriteria Aid for Decisions. Program and Abstracts, Madrid, p. 25 (2000), Meeting of European Working Group Multicriteria Aid for Decisions /51./, Madrid, Spain, 00.03.30-00.03.3

    Google Scholar 

  25. Havránek, T.: The statistical modification ond interpretation of GUHA method. Kybernetika 7, 13–21 (1971)

    MathSciNet  MATH  Google Scholar 

  26. Holeňa, M.: Fuzzy hypotheses for GUHA implications. In: Fuzzy Sets and Systems, vol. 98, pp. 101–125 (1998)

    Google Scholar 

  27. Holeňa, M.: Exploratory data processing using a fuzzy generalization of the GUHA approach. In: Baldwin, et al. (eds.) Fuzzy Logic, pp. 213–229. Willey et Sons, New York (1996)

    Google Scholar 

  28. Holubec jr., L., Topolcan, O., Pikner, R., Pecen, L., Holubec, L., sen, Fínek, J., Ludvíková, M.: Discriminative Level of Tumor Markers after Primary Therapy in Colorectal Carcinoma Patients. In: ISOBM Meeting. Abstract Book, Barcelona, p. 173 (2001)

    Google Scholar 

  29. Lin, W., Alvarez, S.A., Ruiz, C.: Collaborative recommendation via adaptive association rule mining. In: Web-KDD 2000 (2000)

    Google Scholar 

  30. Pecen, L., Pelikán, E., Beran, H., Pivka, D.: Short-term fx market analysis and prediction. In: Neural Networks in Financial Engeneering, pp. 189–196 (1996)

    Google Scholar 

  31. Rauch, J.: Some Remarks on Computer Realisations of GUHA Procedures. International Journal of Man-Machine Studies 10, 23–28 (1978)

    Article  Google Scholar 

  32. Pokorný, D., Rauch, J.: The GUHA-DBS Data Base System. Int. Journ. Math. Machine Studies 15, 289–298 (1981)

    Article  Google Scholar 

  33. Rauch, J.: Logical foundations of mechanizing hypothesis formation from databases (in Czech). PhD. thesis, Mathematical Institute of the Czechoslovak academy of Sciences (1986)

    Google Scholar 

  34. Rauch, J.: Logical problems of statistical data analysis in databases. In: Proc. Eleventh Int. Seminar on Database Management Systems, pp. 53–63 (1988)

    Google Scholar 

  35. Rauch, J.: Logical Calculi for Knowledge Discovery. In: Komorowski, J., Zytkow, J. (eds.) Red, pp. 47–57. Springer, Berlin (1997)

    Google Scholar 

  36. Rauch, J.: Contribution to Logical Foundations of KDD (in Czech). Assoc. Prof. Thesis, Faculty of Informatics and Statistics, University of Economics Prague (1998)

    Google Scholar 

  37. Rauch, J.: Classes of Four-Fold Table Quantifiers. In: Zytkow, J., Quafafou, M. (eds.) Principles of Data Mining and Knowledge Discovery, pp. 203–211. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  38. Rauch, J.: Four-Fold Table Calculi and Missing Information. In: Wang, P.P. (ed.) JCIS 1998 Proceedings, Association for Intelligent Machinery, pp. 375–378 (1998)

    Google Scholar 

  39. Rauch, J., Šimunek, M.: Mining for 4ft association rules. In: Proc. Discovery Science 2000, Kyoto, pp. 268–272. Springer, Heidelberg (2000)

    Google Scholar 

  40. Rauch, J.: Interesting Association Rules and Multi-relational Association Rules. In: Communications of Institute of Information and Computing Machinery, Taiwan, May 2002, vol. 5(2), pp. 77–82 (2002)

    Google Scholar 

  41. Rauch, J.: Mining for Scientific Hypotheses. In: Meij, J. (ed.) Dealing with the data flood. Mining Data, Text and Multimedia, STT/Beweton, The Hague, pp. 73–84 (2002)

    Google Scholar 

  42. Rauch, J., Šimunek, M.: Alternative approach to Mining Association Rules. In: Lin, T.Y., Ohsuga, S. (eds.) IEEE ICDM 2002 Workshop Proceedings The Foundation of Data Mining and Knowledge Discovery, pp. 157–162 (2002)

    Google Scholar 

  43. Šebesta, V., Straka, L.: Determination of Suitable Markers by the GUHA Method for the Prediction of Bleeding at Patients with Chronic Lymphoblastic Leukemia. In: Medicon 1998, Mediterranean Conference on Medical and Biological Engineering and Computing /8./, Lemesos, Cyprus (1998)

    Google Scholar 

  44. Šimunek, M.: Academic KDD Project LISp-Miner. In: Šimunek, M. (ed.) Accepted for publication at Intelligent Systems Design and Applications (ISDA 2003), Tulsa, Oklahoma (2003)

    Google Scholar 

  45. Strossa, P., Rauch, J.: Converting Association Rules into Natural Language - an Attempt. In: Accepted for the presentation at the conference IIS 2003 (Intelligent Information Systems), Zakopane, Poland, June 2–5 (2003)

    Google Scholar 

  46. GUHA+- project, http://www.cs.cas.cz/ics/software.html

  47. LISp-Miner system, http://lispminer.vse.cz

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic

    Petr Hájek, David Coufal & Tomáš Feglar

  2. University of Economics, Prague, Czech Republic

    Jan Rauch

Authors
  1. Petr Hájek
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  2. Jan Rauch
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  3. David Coufal
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  4. Tomáš Feglar
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Editor information

Editors and Affiliations

  1. Dipartimento di Informatica, Università di Torino, Italy

    Rosa Meo

  2. Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milano, Italy

    Pier Luca Lanzi

  3. Nokia Research Center, Nokia Group, P.O.Box 407, FIN-00045, Finland

    Mika Klemettinen

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© 2004 Springer-Verlag Berlin Heidelberg

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Hájek, P., Rauch, J., Coufal, D., Feglar, T. (2004). The GUHA Method, Data Preprocessing and Mining. In: Meo, R., Lanzi, P.L., Klemettinen, M. (eds) Database Support for Data Mining Applications. Lecture Notes in Computer Science(), vol 2682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44497-8_7

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  • DOI: https://doi.org/10.1007/978-3-540-44497-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22479-2

  • Online ISBN: 978-3-540-44497-8

  • eBook Packages: Springer Book Archive

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