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The Rough Set Exploration System

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Part of the book series: Lecture Notes in Computer Science ((TRS,volume 3400))

Abstract

This article gives an overview of the Rough Set Exploration System (RSES). RSES is a freely available software system toolset for data exploration, classification support and knowledge discovery. The main functionalities of this software system are presented along with a brief explanation of the algorithmic methods used by RSES. Many of the RSES methods have originated from rough set theory introduced by Zdzisław Pawlak during the early 1980s.

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References

  1. Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.): RSCTC 2002. LNCS (LNAI), vol. 2475. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  2. Bazan, J.: A Comparison of Dynamic and non-Dynamic Rough Set Methods for Extracting Laws from Decision Tables. In: Skowron, A., Polkowski, L. (eds.) Rough Sets in Knowledge Discovery 1, pp. 321–365. Physica-Verlag, Heidelberg (1998)

    Google Scholar 

  3. Bazan, J., Nguyen, H.S., Nguyen, S.H., Synak, P., Wróblewski, J.: Rough set algorithms in classification problem. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications, pp. 49–88. Physica-Verlag, Heidelberg (2000)

    Google Scholar 

  4. Bazan, J., Nguyen, H.S., Nguyen, T.T., Skowron, A., Stepaniuk, J.: Decision rules synthesis for object classification. In: Orłowska, E. (ed.) Incomplete Information: Rough Set Analysis, pp. 23–57. Physica - Verlag, Heidelberg (1998)

    Google Scholar 

  5. Bazan, J.G., Skowron, A., Ślęzak, D., Wróblewski, J.: Searching for the complex decision reducts: The case study of the survival analysis. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 160–168. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  6. Domingos, P.: Unifying Instance-Based and Rule-Based Induction. Machine Learning 24(2), 141–168 (1996)

    MathSciNet  Google Scholar 

  7. Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)

    MATH  Google Scholar 

  8. Góra, G., Wojna, A.G.: RIONA: a New Classification System Combining Rule Induction and Instance-Based Learning. Fundamenta Informaticae 51(4), 369–390 (2002)

    MATH  MathSciNet  Google Scholar 

  9. Grzymała-Busse, J.: A New Version of the Rule Induction System LERS. Fundamenta Informaticae 31(1), 27–39 (1997)

    MATH  Google Scholar 

  10. Grzymała-Busse, J., Hu, M.: A comparison of several approaches to missing attribute values in data mining. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 378–385. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  11. Hippe, M.: Towards the classification of musical works: A rough set approach. In: [1], pp. 546–553

    Google Scholar 

  12. Komorowski, J., Øhrn, A., Skowron, A.: ROSETTA Rough Sets. In: Kloesgen, W., Zytkow, J. (eds.) Handbook of KDD, pp. 554–559. Oxford University Press, Oxford (2002)

    Google Scholar 

  13. Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough Sets: A tutorial. In: Pal, S.K., Skowron, A. (eds.) Rough Fuzzy Hybridization, pp. 3–98. Springer, Singapore (1999)

    Google Scholar 

  14. Kostek, B., Szczuko, P., Zwan, P.: Processing of musical data employing rough sets and artificial neural networks. In: Tsumoto, S., Slowinski, R., Komorowski, J., Grzymala-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 539–548. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  15. Lazareck, L., Ramanna, S.: Classification of swallowing sound signals: A rough set approach. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 679–684. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  16. Hoa, N.S.: Data regularity analysis and applications in data mining. Ph. D. Thesis, Department of Math., Comp. Sci. and Mechanics. Warsaw University, Warsaw (1999)

    Google Scholar 

  17. Nguyen, S.H., Nguyen, H.S.: Discretization Methods in Data Mining. In: Skowron, A., Polkowski, L. (eds.) Rough Sets in Knowledge Discovery 1, pp. 451–482. Physica Verlag, Heidelberg (1998)

    Google Scholar 

  18. Nguyen, S.H., Skowron, A., Synak, P.: Discovery of data patterns with applications to decomposition and classfification problems. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 2, pp. 55–97. Physica-Verlag, Heidelberg (1998)

    Google Scholar 

  19. Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning. In: Neural and Statistical Classification, Ellis Horwood, London (1994)

    Google Scholar 

  20. Pawlak, Z.: Rough sets. International J. Comp. Inform. Science 11, 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  21. Pawlak, Z.: Rough sets and decision tables. LNCS, vol. 208, pp. 186–196. Springer, Berlin (1985)

    Google Scholar 

  22. Pawlak, Z.: On rough dependency of attributes in information systems. Bulletin of the Polish Academy of Sciences 33, 551–599 (1985)

    MATH  MathSciNet  Google Scholar 

  23. Pawlak, Z.: On decision tables. Bulletin of the Polish Academy of Sciences 34, 553–572 (1986)

    Google Scholar 

  24. Pawlak, Z.: Decision tables – a rough set approach. Bulletin of EATCS 33, 85–96 (1987)

    MATH  Google Scholar 

  25. Pawlak, Z., Skowron, A.: Rough membership functions. In: Yager, R., et al. (eds.) Advances in Dempster Shafer Theory of Evidence, pp. 251–271. Wiley, N.Y (1994)

    Google Scholar 

  26. Pawlak, Z.: In pursuit of patterns in data reasoning from data – the rough set way. In: [1], pp. 1–9 (2002)

    Google Scholar 

  27. Pawlak, Z.: Rough sets and decision algorithms. In: [39], pp. 30–45 (2001)

    Google Scholar 

  28. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer, Dordrecht (1991)

    MATH  Google Scholar 

  29. Peters, J.F., Ramanna, S.: Towards a software change classification system. Software Quality Journal 11(2), 121–148 (2003)

    Article  Google Scholar 

  30. Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowinski, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances in Rough Set Theory, pp. 259–300. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  31. Skowron, A., Polkowski, L.: Synthesis of decision systems from data tables. In: Lin, T.Y., Cercone, N. (eds.) Rough Sets and Data Mining: Analysis for Imprecise Data, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1997)

    Google Scholar 

  32. Skowron, A.: Rough Sets in KDD (plenary talk). 16-th World Computer Congress (IFIP’2000). In: Shi, Z., Faltings, B., Musen, M. (eds.) Proceedings of Conference on Intelligent Information Processing (IIP2000), pp. 1–17. Publishing House of Electronic Industry, Beijing (2000)

    Google Scholar 

  33. Ślęzak, D., Wróblewski, J.: Classification algorithms based on linear combinations of features. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 548–553. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  34. Valdés, J.J., Barton, A.J.: Gene discovery in leukemia revisited: A computational intelligence perspective. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS (LNAI), vol. 3029, pp. 118–127. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  35. Wojna, A.G.: Center-Based Indexing in Vector and Metric Spaces. Fundamenta Informaticae 56(3), 285–310 (2003)

    MATH  MathSciNet  Google Scholar 

  36. Wojnarski, M.: LTF-C: Architecture, Training Algorithm and Applications of New Neural Classifier. Fundamenta Informaticae 54(1), 89–105 (2003)

    MATH  MathSciNet  Google Scholar 

  37. Wróblewski, J.: Genetic algorithms in decomposition and classification problem. In: Skowron, A., Polkowski, L. (eds.) Rough Sets in Knowledge Discovery 1, pp. 471–487. Physica Verlag, Heidelberg (1998)

    Google Scholar 

  38. Wróblewski, J.: Covering with Reducts - A Fast Algorithm for Rule Generation. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 402–407. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  39. Ziarko, W.P., Yao, Y. (eds.): RSCTC 2000. LNCS (LNAI), vol. 2005. Springer, Heidelberg (2001)

    Google Scholar 

  40. The RSES Homepage, http://logic.mimuw.edu.pl/~rses

  41. Report from EUNITE World competition in domain of Intelligent Technologies, http://www.eunite.org/eunite/events/eunite2002/competitionreport2002.htm

  42. The ROSETTA Homepage, http://rosetta.lcb.uu.se/general/

  43. The WEKA Homepage, http://www.cs.waikato.ac.nz/~ml

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Bazan, J.G., Szczuka, M. (2005). The Rough Set Exploration System. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets III. Lecture Notes in Computer Science, vol 3400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427834_2

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  • DOI: https://doi.org/10.1007/11427834_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25998-5

  • Online ISBN: 978-3-540-31850-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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