Evaluating Accuracies of a Trading Rule Mining Method Based on Temporal Pattern Extraction

  • Hidenao Abe
  • Satoru Hirabayashi
  • Miho Ohsaki
  • Takahira Yamaguchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4944)

Abstract

In this paper, we present an evaluation of accuracies of temporal rules obtained from the integrated temporal data mining environment using trading dataset from the Japanese stock market. Temporal data mining is one of key issues to get useful knowledge from databases. However, users often face on difficulties during such temporal data mining process for data pre-processing method selection/construction, mining algorithm selection, and post-processing to refine the data mining process. To get rules that are more valuable for domain experts from a temporal data mining process, we have designed an environment, which integrates temporal pattern extraction methods, rule induction methods and rule evaluation methods with visual human-system interface. Then, we have done a case study to mine temporal rules from a Japanese stock market database for trading. The result shows the availability to find out useful trading rules based on temporal pattern extraction.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Berndt, D.J., Clifford, J.: Using dynamic time wrapping to find patterns in time series. In: Proc. of AAAI Workshop on Knowledge Discovery in Databases, pp. 359–370 (1994)Google Scholar
  2. 2.
    Das, G., King-Ip, L., Heikki, M., Renganathan, G., Smyth, P.: Rule Discovery from Time Series. In: Proc. of International Conference on Knowledge Discovery and Data Mining, pp. 16–22 (1998)Google Scholar
  3. 3.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery: An Overview. In: Advances in Knowledge Discovery and Data Mining, pp. 1–34. AAAI Press / The MIT Press, CA (1996)Google Scholar
  4. 4.
    Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Proc. of the Fifteenth International Conference on Machine Learning, pp. 144–151 (1998)Google Scholar
  5. 5.
    Hirano, S., Tsumoto, S.: Mining Similar Temporal Patterns in Long Time-Series Data and Its Application to Medicine. In: Proc. of the 2002 IEEE International Conference on Data Mining, pp. 219–226 (2002)Google Scholar
  6. 6.
    Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding Motifs in Time Series. In: Proc. of Workshop on Temporal Data Mining, pp. 53–68 (2002)Google Scholar
  7. 7.
    Liu, H., Motoda, H.: Feature selection for knowledge discovery and data mining. Kluwer Academic Publishers, Dordrecht (1998)MATHGoogle Scholar
  8. 8.
    Michalski, R., Mozetic, I., Hong, J., Lavrac, N.: The AQ15 Inductive Leaning System: An Overview and Experiments, Reports of Machine Leaning and Inference Laboratory, MLI-86-6, George Maseon University (1986)Google Scholar
  9. 9.
    Mitchell, T.M.: Generalization as Search. Artificial Intelligence 18(2), 203–226 (1982)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Ohsaki, M., Sato, Y., Yokoi, H., Yamaguchi, T.: A Rule Discovery Support System for Sequential Medical Data - In the Case Study of a Chronic Hepatitis Dataset -. In: ECML/PKDD-2003 Workshop on Discovery Challenge, pp. 154–165 (2003)Google Scholar
  11. 11.
    Ohsaki, M., Kitaguchi, S., Okamoto, K., Yokoi, H., Yamaguchi, T.: Evaluation of Rule Interestingness Measures with a Clinical Dataset on Hepatitis. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 362–373. Springer, Heidelberg (2004)Google Scholar
  12. 12.
    Ohsaki, M., Abe, H., Kitaguchi, S., Kume, S., Yokoi, H., Yamaguchi, T.: Development and Evaluation of an Integrated Time-Series KDD Environment - A Case Study of Medical KDD on Hepatitis-. In: Joint Workshop of Vietnamese Society of Artificial Intelligence, SIGKBS-JSAI, ICS-IPSJ and IEICE-SIGAI on Active Mining, vol. 23 (2004)Google Scholar
  13. 13.
    Quinlan, J.R.: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1992)Google Scholar
  14. 14.
    Quinlan, J.R.: Bagging, Boosting and C4.5. AAAI/IAAI 1, 725–730 (1996)Google Scholar
  15. 15.
    Raymond, W., Ada, F.: Mining top-K frequent itemsets from data streams. Data Mining and Knowledge Discovery 13(2), 193–217 (2006)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)Google Scholar
  17. 17.
    KabuRobo: http://www.kaburobo.jp (in Japanese)

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Hidenao Abe
    • 1
  • Satoru Hirabayashi
    • 2
  • Miho Ohsaki
    • 3
  • Takahira Yamaguchi
    • 4
  1. 1.Department of Medical InformaticsShimane University, School of Medicine 
  2. 2.Graduate School of Science and TechnologyKeio University 
  3. 3.Faculty of EngineeringDoshisha University 
  4. 4.Faculty of Science and TechnologyKeio University 

Personalised recommendations