Nothing Special   »   [go: up one dir, main page]

Skip to main content

Rough-Granular Computing Based Relational Data Mining

  • Conference paper
Advances on Computational Intelligence (IPMU 2012)

Abstract

In this paper, we propose a rough-granular computing framework for mining relational data. We adapt the tolerance rough set model for relational data analysis. We introduce two ways for constructing the universe from relational data. Due to applying granular computing methods, one can overcome problems such as relational data representation and the search space limitation. We also show how the proposed framework can be applied to data mining tasks such as classification.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Bain, M.: Predicate Invention and the Revision of First-Order Concept Lattices. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 329–336. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  2. Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Boston (2003)

    MATH  Google Scholar 

  3. Bargiela, A., Pedrycz, W.: Toward a theory of granular computing for human-centered information processing. IEEE T. Fuzzy Systems 16(2), 320–330 (2008)

    Article  Google Scholar 

  4. De Raedt, L.: Logical and Relational Learning. Springer, Heidelberg (2008)

    Book  MATH  Google Scholar 

  5. Džeroski, S., Lavrač, N.: Relational Data Mining. Springer, Berlin (2001)

    MATH  Google Scholar 

  6. Helft, N.: Inductive generalization: A logical framework. In: Bratko, I., Lavrac, N. (eds.) Progress in Machine Learning-Proceedings of EWSL 1987: 2nd European Working Session on Learning, pp. 149–157. Sigma Press, Wilmslow (1987)

    Google Scholar 

  7. Hońko, P.: Classification of Complex Structured Objects on the Base of Similarity Degrees. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 553–563. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  8. Hońko, P.: Simialrity-based classification in relational databases. Fundam. Inform. 101(3), 187–213 (2010)

    MATH  Google Scholar 

  9. Liu, C., Zhong, N.: Rough problem settings for ILP dealing with imperfect data. Comput. Intell. 17(3), 446–459 (2001)

    Article  MathSciNet  Google Scholar 

  10. Martienne, E., Quafafou, M.: Learning Logical Descriptions for Document Understanding: A Rough Sets-Based Approach. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 202–209. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  11. Midelfart, H., Komorowski, J.: A Rough Set Approach to Inductive Logic Programming. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 190–198. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  12. Milton, R.S., Maheswari, V.U., Siromoney, A.: Rough Sets and Relational Learning. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 321–337. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

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

    MATH  Google Scholar 

  14. Pedrycz, W., Skowron, A., Kreinovich, V.: Handbook of Granular Computing. Wiley & Sons, New York (2008)

    Book  Google Scholar 

  15. Skowron, A., Stepaniuk, J., Swiniarski, R.: Modeling rough granular computing based on approximation spaces. Inf. Sci. 184, 20–43 (2012)

    Article  Google Scholar 

  16. Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundam. Inform., 245–253 (1996)

    Google Scholar 

  17. Stepaniuk, J.: Knowledge discovery by application of rough set models. In: Polkowski, S.T., Lin, T. (eds.) Rough Set methods and applications: New Developments in Knowledge Discovery in Information Systems, pp. 137–233. Physica-Verlag, Heidelberg (2000)

    Google Scholar 

  18. Stepaniuk, J.: Rough-Granular Computing in Knowledge Discovery and Data Mining. SCI, vol. 152. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  19. Stepaniuk, J., Hońko, P.: Learning first-order rules: A rough set approach. Fundam. Inform. 61, 139–157 (2004)

    MATH  Google Scholar 

  20. Yao, Y.Y.: Granular computing: Basic issues and possible solutions. In: Proc. the 5th Joint Conference on Information Sciences, pp. 186–189 (2000)

    Google Scholar 

  21. Zadeh, L.A.: Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Set Syst. 90(2), 111–127 (1997)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hońko, P. (2012). Rough-Granular Computing Based Relational Data Mining. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances on Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31709-5_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31709-5_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31708-8

  • Online ISBN: 978-3-642-31709-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics