Advertisement

Rough Set Tools for Practical Data Exploration

  • Andrzej Janusz
  • Sebastian Stawicki
  • Marcin Szczuka
  • Dominik Ślęzak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9436)

Abstract

We discuss a rough-set-based approach to the data mining process. We present a brief overview of rough-set-based data exploration and software systems for this purpose that were developed over the years. Then, we introduce the RapidRoughSets extension for the RapidMiner integrated software platform for machine learning and data mining, along with RoughSets package for R System – the leading software environment for statistical computing. We conclude with discussion of the road ahead for rough set software systems.

Keywords

Rough sets Data mining software R system RapidMiner 

References

  1. 1.
    Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning about Data. Theory and Decision Library D. Kluwer, Dordrecht (1991)zbMATHGoogle Scholar
  2. 2.
    Riza, L.S., Janusz, A., Bergmeir, C., Cornelis, C., Herrera, F., Ślęzak, D., Benítez, J.M.: Implementing algorithms of rough set theory and fuzzy rough set theory in the R package “RoughSets”. Inf. Sci. 287, 68–89 (2014)CrossRefGoogle Scholar
  3. 3.
    Grzymała-Busse, J.W.: LERS - a data mining system. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 1347–1351. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Tsumoto, S.: Automated induction of medical expert system rules from clinical databases based on rough set theory. Inf. Sci. 112, 67–84 (1998)CrossRefGoogle Scholar
  5. 5.
    Prȩdki, B., Wilk, S.: Rough set based data exploration using ROSE system. In: Raś, Z.W., Skowron, A. (eds.) ISMIS 1999. LNCS, vol. 1609, pp. 172–180. Springer, Heidelberg (1999) CrossRefGoogle Scholar
  6. 6.
    Bazan, J., Szczuka, M.S.: The rough set exploration system. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 37–56. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  7. 7.
    Komorowski, J., Øhrn, A., Skowron, A.: Case studies: public domain, multiple mining tasks systems: ROSETTA rough sets. In: Klösgen, W., Żytkow, J.M. (eds.) Handbook of Data Mining and Knowledge Discovery, pp. 554–559. Oxford University Press, Oxford (2002)Google Scholar
  8. 8.
    Wang, G., Zheng, Z., Zhang, Y.: RIDAS - a rough set based intelligent data analysis system. In: Proceedings of ICMLC 2002, vol. 2, pp. 646–649. IEEE (2002)Google Scholar
  9. 9.
    Wojnarski, M.: Debellor: a data mining platform with stream architecture. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 405–427. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  10. 10.
    Wu, M., Nakata, M., Sakai, H.: An overview of the getRNIA system for non-deterministic data. In: Watada, J., Jain, L.C., Howlett, R.J., Mukai, N., Asakura, K., (eds.) Proceedings of Procedia Computer Science KES 2013, vol. 22, pp. 615–622. Elsevier (2013)Google Scholar
  11. 11.
    Zhang, J., Li, T., Chen, H.: Composite rough sets for dynamic data mining. Inf. Sci. 257, 81–100 (2014)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  13. 13.
    Clark, P., Niblett, T.: The CN2 Induction Algorithm. Mach. Learn. 3(4), 261–283 (1989)Google Scholar
  14. 14.
    Michalski, R.S., Kaufman, K.A.: Learning patterns in noisy data: The AQ approach. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds.) ACAI 1999. LNCS (LNAI), vol. 2049, pp. 22–38. Springer, Heidelberg (2001) CrossRefGoogle Scholar
  15. 15.
    Janusz, A., Ślęzak, D.: Random probes in computation and assessment of approximate reducts. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds.) RSEISP 2014. LNCS, vol. 8537, pp. 53–64. Springer, Heidelberg (2014) Google Scholar
  16. 16.
    Kabiesz, J., Sikora, B., Sikora, M., Wróbel, Ł.: Application of rule-based models for seismic hazard prediction in coal mines. Acta Montanistica Slovaca 18(4), 262–277 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  • Andrzej Janusz
    • 1
  • Sebastian Stawicki
    • 1
  • Marcin Szczuka
    • 1
  • Dominik Ślęzak
    • 1
    • 2
  1. 1.Institute of MathematicsUniversity of WarsawWarsawPoland
  2. 2.Infobright Inc.WarsawPoland

Personalised recommendations