Skip to main content
Springer Nature Link
Log in

FCBI: An Efficient User-Friendly Classifier Using Fuzzy Implication Table

  • Conference paper
Advances in Databases and Information Systems (ADBIS 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2798))

  • 420 Accesses

Abstract

In the past few years, exhaustive search method under the name of association rule mining has been widely used in the field of classification. However, such kind of methods usually produce too many crisp if-then rules and is not an efficient way to represent the knowledge, especially in real-life data mining application. In this paper, we propose a novel associative classification method called FCBI, i.e., Fuzzy Classification Based on Implication. This method partitions the original data set into fuzzy table without discretization on continuous attributes, the rule generation is performed in the relational database system by using fuzzy implication table. The unique features of this method include its high training speed and simplicity in implementation. Experiment results show that the classification rules generated are meaningful and explainable.

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

Access this chapter

Log in via an institution

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

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. Liu, B., et al.: Integrating classification and association rule mining. In: Proceedings of the Fourth ACM SIGKDD KDD 1998, New York, USA, pp. 80–86 (1998)

    Google Scholar 

  2. Kuok, C.M., Fu, A.W.-C., Wong, M.H.: Mining Fuzzy Association Rules in Databases. SIGMOD Record 27(1), 41–46 (1998)

    Article  Google Scholar 

  3. Wong, C., Shiu, S., Pal, S.: Mining Fuzzy Association Rules for Web access Case Adaptation. In: Proceedings of the Workshop Program at the Fourth International Conference on Case-Based Reasoning, Vancouver, Canada (2001)

    Google Scholar 

  4. Meretakis, D., Wüthrich, B.: Extending naïve Bayes classifiers using long itemsets. In: Proceedings of 5th ACM SIGKDD KDD 1999, California, pp. 165–174 (1999)

    Google Scholar 

  5. Zhou, D., et al.: Bayesian classifier based on discretized continuous feature space. In: Proceedings of 4th International Conference on Signal Processing, vol. 2, pp. 1225–1228 (1998)

    Google Scholar 

  6. Nauck, D.D.: Using symbolic data in neuro-fuzzy classification. In: Proceedings of NAFIPS 1999, New York, pp. 536–540 (1999)

    Google Scholar 

  7. Hüllermeier, E.: Implication-based fuzzy association rules. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 241–252. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  8. Dong, G., Zhang, X., Wong, L., Li, J.: CAEP: Classification by aggregating emerging patterns. In: Arikawa, S., Furukawa, K. (eds.) DS 1999. LNCS, vol. 1721, p. 30. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  9. Lu, H., Liu, H.-Y.: Decision Tables: Scalable Classification Exploring RDBMS Capabilities. In: VLDB 2000, pp. 373–384 (2000)

    Google Scholar 

  10. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)

    Google Scholar 

  11. Ali, K., Manganaris, S., Srikant, R.: Partial classification using association rules. In: ACM SIGKDD 1997, pp. 115–118 (1997)

    Google Scholar 

  12. Wang, K., Zhou, S., He, Y.: Growing decision tree on support-less association rules. In: KDD 2000, Boston, MA, pp. 265–269 (2000)

    Google Scholar 

  13. Xiong, N., et al.: Learning Premises of Fuzzy Rules for Knowledge Acquisition in Classification Problems. Knowledge and Information Systems 4(1), 96–111 (2002)

    Article  MATH  Google Scholar 

  14. Clark, P., Niblett, T.: The CN2 induction algorithm. Machine Learning 3, 261–283 (1989)

    Google Scholar 

  15. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: VLDB 1994, Santiago, Chile, pp. 487–499 (1994)

    Google Scholar 

  16. Bayardo, R.: Brute-Force mining of high confidence classification rules. In: Proceedings of 3rd ACM SIGKDD KDD 1997, 123–126 (1997)

    Google Scholar 

  17. Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley, Chichester (1973)

    MATH  Google Scholar 

  18. Mendes, R.R.F., et al.: Discovering Fuzzy Classification Rules With Genetic Programming and Co-evolution, In: PKDD 2001, pp. 314–325 (2001)

    Google Scholar 

  19. Pal, S.K., Mitra, S.: Multilayer perceptron, fuzzy sets and classification. IEEE Trans. Neural Networks 3, 683–697 (1992)

    Article  Google Scholar 

  20. Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proc. IJCAI, pp. 800–805 (1989)

    Google Scholar 

  21. Au, W.-H., Chan, K.C.C.: FARM: a data mining system for discovering fuzzy association rules. In: Fuzzy Systems Conference Proceedings (FUZZ-IEEE 1999), vol. 3, pp. 1217–1222 (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, National University of Singapore, 3 Science Drive 2, Singapore, 117543

    Chen Zheng

  2. Department of Business Administration, Alfred University, 38 West University Street, NY, 14802, USA

    Li Chen

Authors
  1. Chen Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Informatics Problems, Russian Academy of Science,  

    Leonid Kalinichenko

  2. Institute of Computer Science III, University of Bonn, Roemerstr. 164, D-53117, Bonn, Germany

    Rainer Manthey

  3. Institute of Computer Science and Applied Mathematics, Christian-Albrechts-University of Kiel, Olshausenstr. 40, 24098, Kiel, Germany

    Bernhard Thalheim

  4. Department of InformationTechnology/Mathematics, University ofApplied Sciences, Friedrich-List-Platz 1, 01069, Dresden, Germany

    Uwe Wloka

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zheng, C., Chen, L. (2003). FCBI: An Efficient User-Friendly Classifier Using Fuzzy Implication Table. In: Kalinichenko, L., Manthey, R., Thalheim, B., Wloka, U. (eds) Advances in Databases and Information Systems. ADBIS 2003. Lecture Notes in Computer Science, vol 2798. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39403-7_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-39403-7_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20047-5

  • Online ISBN: 978-3-540-39403-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics