Data Mining — The Polish Experience

  • Eugeniusz Gatnar
  • Dorota Rozmus
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Data mining is used to turn data into information and it links several fields including statistics, artificial intelligence, database management, machine learning, pattern recognition and data visualization. It became a field of interest in the early 90s. Since then the interest in data mining has spread worldwide. Also in Poland there are research institutes working in this field, software developers offering specialized software and successful applications of data mining techniques. In this paper we present the development of data mining in Poland.


Data Mining Data Mining Technique Polish Experience Data Mining Method Rule Discovery 
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.


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  1. FRIEDMAN, J. (1997): Data Mining and Statistics: What’s the Connection ? The 29th Symposium on the Interface, Houston, TX.Google Scholar
  2. GATNAR, E. (1997): Data Mining and Statistical Data Analysis. Statistical Revue, 2, 309–316 (in Polish).MATHGoogle Scholar
  3. GATNAR, E. (2001): The Nonparametric Method of Discrimination and Regression: Decision Trees. PWN Publishers, Warsaw (in Polish).Google Scholar
  4. LASEK, M. (2002): Data Mining: Applications in Bank Clients’ Scoring. Management and Finance Publishers, Warsaw (in Polish).Google Scholar
  5. MICHALEWICZ, Z. and SCHMIDT, M. (2001): Evolutionary Algorithms. In: Encyclopedia of Information Systems. Academic Press, New York.Google Scholar
  6. MITROWSKI, M. (2000): Data Exploration Systems. Master thesis, Warsaw University (in Polish).Google Scholar
  7. PAWLAK, Z. (1992): Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer, Dordrecht.Google Scholar
  8. POLKOWSKI, L. and SKOWRON, A. (2001), Rough Set Approach to Computation. Computational Intelligence 17, 472–492.MathSciNetCrossRefGoogle Scholar
  9. PREDKI, B., SLOWINSKI, R., STEFANOWSKI, J., SUSMAGA, R., and WILK, S. (1998): ROSE-Software Implementation of the Rough Set Theory. In: L. Polkowski and A. Skowron (Eds.): Rough Sets and Current Trends in Computing, Lecture Notes in Artificial Intelligence, vol. 1424, Springer, Berlin, 605–608.Google Scholar
  10. PREDKI, B. and WILK, S. (1999): Rough Set Based Data Exploration Using ROSE System. In: Z. Ras and A. Skowron (Eds.): Foundations of Intelligent Systems, Lecture Notes in Artificial Intelligence, vol. 1609, Springer, Berlin, 172–180.Google Scholar
  11. RAS, Z. and WIECZORKOWSKA, A. (2000): Mining for action-rules in large decision tables classifying customers. Advances in Soft Computing, Physica-Verlag, Heidelberg, 55–64.Google Scholar
  12. RESZKA, P. (2001): Knowledge Discovery in Large Datasets. Master thesis, Warsaw University (in Polish).Google Scholar
  13. SKOWRON, A., SURAJ, Z., PETERS, J., and RAMANA, S. (2001): Sensor, Filter, and Fusion Models with Rough Petri Nets. Fundamenta Informaticae 47, 307–323.MathSciNetGoogle Scholar
  14. STEPANIUK, J. (1999): Knowledge Discovery by Application of Rough Sets Models. Phd. thesis, Department of Theoretical Foundations of Computer Science, Polish Academy of Sciences (in Polish).Google Scholar
  15. SZCZUKA, M. (2000): Symbolic Methods and Neural Networks for Building Classifiers. Phd. thesis, Warsaw University (in Polish).Google Scholar
  16. TADEUSIEWICZ, R. (1995): Neural Networks. PLJ Publishers, Warsaw (in Polish).Google Scholar
  17. WITKOWSKA, D. (2000): Artificial Neural Networks in economic analysis. Management Publishers, Lodz (in Polish).Google Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 2005

Authors and Affiliations

  • Eugeniusz Gatnar
    • 1
  • Dorota Rozmus
    • 1
  1. 1.Institute of StatisticsKatowice University of EconomicsKatowicePoland

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