Reduction of Discriminant Rules Based on Frequent Item Set Calculation

  • María C. Fernández-Baizán
  • Ernestina Menasalvas Ruiz
  • Juan Francisco Martínez Sarrías
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 84)


Reduction of the number of attributes to calculate rules in large databases is of great interest in data mining In this paper, we propose a method for reducing the number of attributes in rules using frequent item sets calculation. The method is based in a basic step model. In our approach algorithms are divided in atomic operations that have been called basic steps so that it is easier to optimize the execution of any algorithm. We also present the implementation of this approach in Damisys what demonstrates that our approach is implementable and effective dealing with large datasets.


Association Rule Basic Step Mining Association Rule Frequent Item Decision Attribute 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [Agrawal93a]
    R. Agrawal, T. Imielinski, A. Swami Mining association rules between sets of Item in large Databases, Proceedings of ACM SIGMOD, pages 207–216, May 1993Google Scholar
  2. [Agrawal93b]
    R. Agrawal, Mining Association rules between sets of items in large databases In Proceedings of ACM SIGMOD International Conference on Management of data, pp.207–216, Washington DC, 1993Google Scholar
  3. [Agrawal93c]
    R. Agrawal, T. Imielinski, A. Swani Database Mining: A performance perspective IEEE Transactions on Knowledge and Data Engineering 5(6) pp. 914925, December 1993. An special Issue on Learning and Discovery in knowledge Based DatabasesGoogle Scholar
  4. [Agrawal94]
    R. Agrawal, R. Srinkant Fast Algorithms for Mining Association Rules Chile 1994Google Scholar
  5. Agrawal95] R. Agrawal, K. Shim Developing tightly-coupled Data Mining applications on a Relational database system In Proceedings of KDD’96, pp. 287–291 Orlando, July 1996Google Scholar
  6. [Agrawal96a]
    R. Agrawal, Shafer Parallel Mining of association rules IEEE Transactions on Knowledge and Data Engineering 8(6)Google Scholar
  7. [Agrawal96b]
    R. Agrawal, H. Mannila, R. Srikant, Fast Discovery of Association Rules Advances in Knowledge Discovery and Data Mining. U. Fayyad et al. Ed., AAAI/MIT Press 1996.Google Scholar
  8. [Agrawal97]
    R. Agrawal, Q. Vu, R. Skrinkant Mining association rules with item constrains Proc. del 3rd Int’l Conference on Knowledge Discovery in DataBases and Data Mining, Newport Beach, California, august 1997.Google Scholar
  9. [Bayardo98]
    Efficiently Mining Long Patterns from Databases Proc. de ACM SIGMOD Conference on Management of Data, Seattle, june 1998Google Scholar
  10. [Cheung]
    D. W. Cheung, J. Han, V. T. Ng Y. Fu A Fast Distributed Algorithm for Mining Association Rules Google Scholar
  11. [Fayyad96a]
    U. Fayyad, G. Piatestky-Shaphiro, P. Smyth Knowledge Discovery and Data Mining: Towards a Unifying Framework In Proceedings The Second International Conference on Knowledge discovery and Data Mining, pp. 82–88. August 1996Google Scholar
  12. [Fayyad96b]
    U. Fayyad, G. Piatestky-Shaphiro, P. Smyth, R. Uthurusamy From Data Mining to Knowledge Discovery: An Overview, Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press 1996, pp. 1–30Google Scholar
  13. [Fernandez97a]
    M. Fernandez Baizan, E. Menasalvas, J. Pea, J. PardoA new approach for the calculation of reducts in large databases In Proc. JICS’97 pp.340–344, Carolina 1997Google Scholar
  14. [Fernandez00]
    M. Fernandez Baizan, E. Menasalvas, J. Pea Integrating RDBMS and Data Mining Capabilities Using Rough Sets, Knowledge Management in Fuzzy Databases, De Pons, Vila, Kacprzyk. Springer Verlag, January 2000Google Scholar
  15. M. Fernandez Baizan, E. Menasalvas, E. Santos, R. Portaencasa, C. LLera. The Lattice of generalizations in a KDD process In Proceedings, EMCSR’98, Viena, April-98, pp. 181–183Google Scholar
  16. [Fernandez99]
    M. Fernandez Baizân, J.Pea, J. Martinez-Sarrias, O. Delgado, J. Ignacio Lopez, M. Luna, J.F. Borja Damisys: An Overview In Proceedings, Dexa’99, Florence, August 30–1999Google Scholar
  17. [Lin96]
    T.Y. Lin, Rough Set Theory in Very Large Databases In Proceedings, CESA’96, Lille, July-96, pp.936–941Google Scholar
  18. [Pawlak82]
    Z. Pawlak, Rough Sets, International Journal of Computer and Information Sciences Vol 11, n. 5 1982Google Scholar
  19. [Pawlak86]
    Z. Pawlak, On Decision Tables, Bulletin of The Polish Academy of Sciences Mathematics vol. 34, No. 7, 1986, pp. 563–571.MathSciNetGoogle Scholar
  20. [Pawlak88]
    Z. Pawlak M. Novotny, Independence of Attributes, Bulletin of The Polish Academy of Sciences Mathematics vol. 36, No.7, 1988, pp. 459–465.Google Scholar
  21. [Pawlak91]
    Z. Pawlak, Rough Sets - Theoretical Aspects of Reasoning about Data, Kluwer Academic, 1991.Google Scholar
  22. [Pawlak92]
    Z. Pawlak, Rough Sets and their applications, ICS Research Report 18/92 Warsaw University of TechnologyGoogle Scholar
  23. [Pawlak93]
    Z. Pawlak, Information Systems-Theoretical foundations, Information systems, 6, No. 4, 1993, pp. 299–297.Google Scholar
  24. [Skowron9l]
    A. Skowron, C. Rauszer, The Discernibility matrices and Functions in Information System „ ICS PAS Report 1/91, Technical University of Warsaw 1991, pp. 1–44Google Scholar
  25. [Skowron92]
    A. Skowron, The discernibility matrices and functions in information systems. Decision Support by Experience, R. Slowinski(ed.) Kluwer Academic Publishers,1992Google Scholar
  26. [Tsumoto96]
    S. Tsumoto Incremental Learning of Probabilistic Rules from clinical Databases based on Rough Sets Theory, In Proceedings IPMU’96 vol. 3, pp. 1457–1462. Granada 1996Google Scholar
  27. [Walkulicz-Deja96]
    A. Walkulicz-Deja et al. Applying Rough Sets to diagnose in Children’s Neurology In Proceedings IPMU’96 vol. 3, pp. 1463–1467. Granada 1996Google Scholar
  28. [Ziarko93a]
    W. Ziarko, Variable Precision Rough Sets Model, Journal of Computer and System Sciences, vol. 46. 1993, 39–59MathSciNetMATHCrossRefGoogle Scholar
  29. [Ziarko93b]
    W. Ziarko,R. Golan, D. Edwards An application of Datalogic/R Knowledge Discovery tool to identify Strong Predictive rules in Stock Market Data, Proceedings of AAAI Workshop on Knowledge Discovery in Databases, 1993, pp 89–101.Google Scholar
  30. [Ziarko93c]
    W. Ziarko, Variable precision Rough Set model Journal of computer and systems sciences, 46, 39–59. 1993MathSciNetMATHCrossRefGoogle Scholar
  31. [Ziarko94]
    W. Ziarko and N. Shan, KDD-R: A comprehensive system for Knowledge Discovery in Databases using Rough Sets In Proceedings of the International Workshop on Rough Sets and Soft Computing RSSC’94, pp. 164–173Google Scholar
  32. [Ziarko95a]
    W. Ziarko, N. Shan On Discovery of Attribute Interactions and Domain Classifications, CSC’95 23 Annual Computer Science Conference on Rough Sets and Data MiningGoogle Scholar
  33. [Ziarko95b]
    Ziarko W. Data-Based Acquisition and incremental modification of classification rules Computational Intelligence. pp. 357–370, 1995Google Scholar
  34. [Ziarko96]
    W. Ziarko, Discovering classification Knowledge in databases using Rough Sets In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996, pp. 271–274Google Scholar
  35. [Zighed97]
    D.A. Zighed, R. Rakotomalala, F. Feschet, In Proceedings The Third Conference of Knowledge Discovery and Data Mining (KDD-97), Edited by D. Heckerman, H. Mannila, D. Pregibon and R. Uthurusamy, pp. 295Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • María C. Fernández-Baizán
    • 1
  • Ernestina Menasalvas Ruiz
    • 1
  • Juan Francisco Martínez Sarrías
    • 1
  1. 1.Departamento de Lenguajes y Sistemas Informáticos e Ingeniería del Software, Facultad de InformáticaUniversidad Politecnica de MadridMadridSpain

Personalised recommendations