Skip to main content
SpringerLink
Log in
Book cover

Rough-Neural Computing pp 689–722Cite as

A Hybrid Model for Rule Discovery in Data

  • Chapter

  • 258 Accesses

Part of the book series: Cognitive Technologies ((COGTECH))

Summary

This chapter presents a hybrid model for rule discovery in real-world data with uncertainty and incompleteness. The hybrid model is created by introducing an appropriate relationship between deductive reasoning and a stochastic process and extending the relationship to include abduction. Furthermore, a generalization distribution table (GDT), which is a variant of the transition matrix of a stochastic process, is defined. Thus, the typical methods of symbolic reasoning such as deduction, induction, and abduction, as well as the methods based on soft computing techniques such as rough sets, fuzzy sets, and granular computing can be cooperatively used by taking the GDT and/or the transition matrix of a stochastic process as media. Ways of implementing the hybrid model are also discussed.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Reference

  1. R. Andrews, J. Diederich, A. B. Tickle. Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems, 8(6), 373–389, 1995.

    Article  Google Scholar 

  2. M. Banerjee, S. Mitra, S. K. Pal. Rough fuzzy MLP: Knowledge encoding and classification. IEEE Transactions on Neural Networks, 9(6): 1203–1216, 1998.

    Article  Google Scholar 

  3. J. Cendrowska. PRISM: An algorithm for inducing modular rules. International Journal of Man-Machine Studies, 27: 349–370, 1987.

    Article  MATH  Google Scholar 

  4. J.Z. Dong, N. Zhong, S. Ohsuga. Probabilistic rough induction: the GDT-RS methodology and algorithms. In Z.W. Ras, A. Skowron, editors, Foundations of Intelligent Systems, LNAI 1609, 621–629, Springer, Berlin, 1999.

    Chapter  Google Scholar 

  5. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy, editors. Advances in Knowledge Discovery and Data Mining. AAAI Press, Menlo Park, CA, 1996.

    Google Scholar 

  6. R.A. Johnson, D.W. Wichern. Applied Multivariate Statistical Analysis. Prentice-Hall, Englewood Cliffs, NJ, 1992.

    Google Scholar 

  7. N. Lavrac, S. Dzeroski, I. Bratko. Handling imperfect data in inductive logic programming. In L. de Raedt, editor, Advances in Inductive Logic Programming, 48–64, IOS, Amsterdam, 1996.

    Google Scholar 

  8. T.Y. Lin, N. Cercone. Rough Sets and Data Mining: Analysis of Imprecise Data. Kluwer, Dordrecht, 1997.

    MATH  Google Scholar 

  9. C. Liu, N. Zhong, S. Ohsuga. Constraint ILP and its application to KDD. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’97). The Workshop on Frontiers of ILP, 103–104, Nagoya, Japan, 1997.

    Google Scholar 

  10. C. Liu, N. Zhong. Rough problem settings for inductive logic programming. In N. Zhong, A. Skowron, S. Ohsuga, editors, New Directions in Rough Sets, Data Mining, and Granular-Soft Computing, LNAI 1711, 168–177, Springer, Berlin, 1999.

    Google Scholar 

  11. T.M. Mitchell. Machine Learning. McGraw-Hill, New York, 1997.

    MATH  Google Scholar 

  12. S. Muggleton. Inductive logic programming. New Generation Computing, 8(4): 295–317, 1991.

    Article  MATH  Google Scholar 

  13. S.H. Nguyen, H.S. Nguyen. Quantization of real value attributes for control problems. In Proceedings of the 4th European Congress on Intelligent Techniques and Soft Computing (EUFIT’96), 188–191, Verlag Mainz, Aachen, 1996.

    Google Scholar 

  14. S.H. Nienhuys-Cheng, R.Wolf, editors. Foundations of Inductive Logic Programming. LNAI 1228, Springer, Berlin, 1997.

    Google Scholar 

  15. S. Ohsuga, H. Yamauchi. Multi-layer logic: A predicate logic including data structure as a knowledge representation language. New Generation Computing, 3(4): 403–439, 1985.

    Article  MATH  Google Scholar 

  16. S. Ohsuga. Symbol processing by non-symbol processor. In Proceedings of the 4th Pacific Rim International Conference on Artificial Intelligence (PRICAI’96), 193–205, 1996.

    Google Scholar 

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

    MATH  Google Scholar 

  18. G. Polya. Mathematics and Plausible Reasoning: Patterns of Plausible Inference. Princeton University Press, Princeton, NJ, 1968.

    Google Scholar 

  19. J. R. Quinlan. Induction of decision trees. Machine Learning, 1: 81–106, 1986.

    Google Scholar 

  20. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.

    Google Scholar 

  21. D.E. Rumelhart, G.E. Hinton, R.J. Williams. Learning internal representations by back-propagation errors. Nature, 323: 533–536, 1986.

    Article  Google Scholar 

  22. A. Skowron, C. Rauszer. The discernibility matrices and functions in information systems. In R. Slowi’nski, editor, Intelligent Decision Support: Handbook of Advances of Rough Sets Theory, 331–362, Kluwer, Dordrecht, 1992.

    Chapter  Google Scholar 

  23. Y.Y. Yao, N. Zhong. An analysis of quantitative measures associated with rules. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’99), LNAI 1574, 479–488, Springer, Berlin, 1999.

    Google Scholar 

  24. Y.Y. Yao, N. Zhong. Potential applications of granular computing in knowledge discovery and data mining. In Proceedings of the 5th International Conference on Information Systems Analysis and Synthesis (IASA’99), edited in the invited session on Intelligent Data Mining and Knowledge Discovery, 573–580, International Institute of Informatics and Systemics, Orlando, Fl, 1999.

    Google Scholar 

  25. Y.Y. Yao. Granular computing: Basic issues and possible solutions. In Proceedings of the Joint Conference on Intelligence Systems (JCIS 2000), invited session on Granular Computing and Data Mining, 1: 186–189, Association for Intelligent Machinery, Atlantic City, NJ, 2000.

    Google Scholar 

  26. L.A. Zadeh. Fuzzy sets and information granularity. In N. Gupta, R. Ragade, R.R. Yager, editors, Advances in Fuzzy Set Theory and Applications, 3–18, North-Holland, Amsterdam, 1979.

    Google Scholar 

  27. L.A. Zadeh. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90: 111–127, 1997.

    Article  MathSciNet  MATH  Google Scholar 

  28. N. Zhong, S. Ohsuga. Using generalization distribution tables as a hypothesis search space for generalization. In Proceedings of the 4th International Workshop on Rough Sets, Fuzzy Sets,and Machine Discovery (RSFD’96), 396–403, University of Tokyo, Tokyo, Japan, 1996.

    Google Scholar 

  29. N. Zhong, J.Z. Dong, S. Ohsuga. Data mining: A probabilistic rough set approach. In Proceedings of the 1st International Conference on Rough Sets and Current Trends in Computing (RSCTC’98), LNAI 1424, 127–146, Springer, Berlin, 1998.

    Google Scholar 

  30. N. Zhong, J.Z. Dong, S. Fujitsu, S. Ohsuga. Soft techniques for rule discovery in data. Transactions of Information Processing Society of Japan, 39(9): 2581–2592, 1998.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Engineering, Maebashi Institute of Technology, 460-1, Kamisadori-Cho, Maebashi-City, 371, Japan

    Ning Zhong & Ju-Zhen Dong

  2. School of Computer Science, Beijing Polytechnic University, Beijing, 100022, P.R.China

    Chunnian Liu

  3. Department of Information and Computer Science, School of Science and Engineering, Waseda University, 3-4-1 Okubo Shinjuku-Ku, Tokyo 169, Japan

    Setsuo Ohsuga

Authors
  1. Ning Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chunnian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ju-Zhen Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Setsuo Ohsuga
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Indian Statistical Institute, Machine Intelligence Unit, 203 Barrackpore Trunk Road, Calcutta, 700 035, India

    Sankar K. Pal

  2. Department of Mathematics and Computer Science, University of Warmia and Mazury, Zolnierska 14, Olsztyn, Poland

    Lech Polkowski

  3. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Zhong, N., Liu, C., Dong, JZ., Ohsuga, S. (2004). A Hybrid Model for Rule Discovery in Data. In: Pal, S.K., Polkowski, L., Skowron, A. (eds) Rough-Neural Computing. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18859-6_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-18859-6_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-62328-8

  • Online ISBN: 978-3-642-18859-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation