Advertisement

A Distance-Based Approach to Find Interesting Patterns

  • Chen Zheng
  • Yanfen Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2737)

Abstract

One of the major problems in knowledge discovery is producing too many trivial and uninteresting patterns. The measurement of interestingness is divided into subjective and objective measures and used to address the problem. In this paper, we propose a novel method to discover interesting patterns by incorporating the domain user’s preconceived knowledge. The prior knowledge constitutes a set of hypothesis about the domain. A new parameter called the distance is proposed to measure the gap between the user’s existing hypothesis and system-generated knowledge. To evaluate the practicality of our approach, we apply the proposed approach through some real-life data sets and present our findings.

Keywords

Fuzzy Rule Linguistic Term Interesting Pattern Attribute Weight Antecedent Part 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Matheus, C.J., Piatesky-Shapiro, G., Mcneil, D.: An application of KEFIR to the analysis of healthcare information. In: Proceedings of the AAAI 1994 Workshop on Knowledge Discovery in Databases (1994)Google Scholar
  2. 2.
    Hong, J., Mao, C.: Incremental discovery of rules and structure by hierachical and parallel clustering. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases. AAAI/MIT Press (1991)Google Scholar
  3. 3.
    Klemetinen, M., Mannila, H., et al.: Finding interesting rules from large sets of discovered association rules. In: Proceedings of the Third International Conference on Information and Knowledge Management, pp. 401–407 (1994)Google Scholar
  4. 4.
    Liu, B., et al.: Integrating Classification and Association Rule Mining. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pp. 80–86 (1998)Google Scholar
  5. 5.
    Liu, B., Hsu, W.: Post-Analysis of Learned Rules. In: Proc. Of the thirteenth National Conf. On Artificial Intelligence (AAAI 1996), pp. 828–834 (1996)Google Scholar
  6. 6.
    Liu, B., Hsu, W., Chen, S.: Using General Impressions to Analyze Discovered Classification Rules. In: Proc. of the Third Intl. Conf. On knowledge Discovery and Data Mining, pp. 31–36 (1997)Google Scholar
  7. 7.
    Major, J., Mangano, J.: Selecting among rules induced from a hurricane database. In: KDD 1993, pp. 28–41 (1993)Google Scholar
  8. 8.
    Kaya, M., et al.: Efficient Automated Mining of Fuzzy Association Rules. DEXA 133–142 (2002)Google Scholar
  9. 9.
    Padmanabhan, B., Tuzhilin, A.: On the Discovery of Unexpected Rules in Data Mining Applications. In: Procs. of the Workshop on Information Technology and Systems, pp. 81–90 (1997)Google Scholar
  10. 10.
    Padmanabhan, B., Tuzhilin, A.: A beliefe-driven method for discovering unexpectedpatterns. In: Proc.of the Fourth International Conference on Knowledge Discovery and Data Mining, pp. 27–31 (1998)Google Scholar
  11. 11.
    Piatesky-Shapiro, G., Matheus, C.: The interestingness of deviations. KDD 1994, 25–36 (1994)Google Scholar
  12. 12.
    Piatetsky-Shapiro, G., Matheus, C., Smyth, P., Uthurusamy, R.: KDD 1993: progress and challenges..., AI magazine, Fall, pp. 77–87 (1994)Google Scholar
  13. 13.
    Smyth, P., Goodman, R.M.: Rule induction using information theory. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases. AAAI/MIT Press (1991)Google Scholar
  14. 14.
    Silberschatz, A., Tuzhilin, A.: On Subjective Measures of Interestingness in Knowledge Discovery. In: Proc. of the First International Conference on Knowledge Discovery and Data Mining, pp. 275–281 (1995)Google Scholar
  15. 15.
    Silberschatz, A., Tuzhilin, A.: What Makes Patterns Interesting in Knowledge Discovery Systems. IEEE Trans. on Know. and Data Engineering. Spec. Issue on Data Mining 5(6), 970–974 (1996)Google Scholar
  16. 16.
    Dhar, V., Tuzhilin, A.: Abstract-driven pattern discovery in databases. IEEE Transactions on Knowledge and Data Engineering 5(6) (1993)Google Scholar
  17. 17.
    Zadeh, L.A.: Similarity relations and fuzzy orderings. Inf. Sci. 3, 159–176 (1971)zbMATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Zimmermann, H.J.: Fuzzy set theory and its applications. Kluwer Academic Publishers, Dordrecht (1991)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Chen Zheng
    • 1
  • Yanfen Zhao
    • 2
  1. 1.Department of Computer ScienceNational University of SingaporeSingapore
  2. 2.China Construction BankFujianP.R.China

Personalised recommendations