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Implementing an Integrated Time-Series Data Mining Environment Based on Temporal Pattern Extraction Methods: A Case Study of an Interferon Therapy Risk Mining for Chronic Hepatitis

  • Hidenao Abe
  • Miho Ohsaki
  • Hideto Yokoi
  • Takahira Yamaguchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4012)

Abstract

In this paper, we present the implementation of an integrated time-series data mining environment. Time-series data mining is one of key issues to get useful knowledge from databases. With mined time-series patterns, users can aware not only positive results but also negative result called risk after their observation period. However, users often face difficulties during time-series data mining process for data pre-processing method selection/construction, mining algorithm selection, and post-processing to refine the data mining process as other data mining processes. It is needed to develop a time-series data mining environment based on systematic analysis of the process. To get more valuable rules for domain experts from a time-series data mining process, we have designed an environment which integrates time-series pattern extraction methods, rule induction methods and rule evaluation methods with active human-system interaction. After implementing this environment, we have done a case study to mine time-series rules from blood and urine biochemical test database on chronic hepatitis patients. Then a physician has evaluated and refined his hypothesis on this environment. We discuss the availability of how much support to mine interesting knowledge for an expert.

Keywords

Chronic Hepatitis Domain Expert Rule Induction Pattern Extraction Data Mining Process 
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.

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References

  1. 1.
    Abe, H., Yamaguchi, T.: Constructive Meta-Learning with Machine Learning Method Repositories. In: Proc. of the seventeenth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, pp. 502–511 (2004)Google Scholar
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Daubechies, I.: Ten lectures on wavelets, Society for Industrial and Applied Mathematics (1992)Google Scholar
  5. 5.
    Fayyad, U.M., Piatcktsky-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, Menlo Park (1996)Google Scholar
  6. 6.
    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
  7. 7.
    Hirano, S., Tsumoto, S.: Mining Similar Temporal Patterns in Long Time-Series Data and Its Application to Medicine. In: Proc. of the, IEEE International Conference on Data Mining, 2002, pp. 219–226 (2002)Google Scholar
  8. 8.
    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
  9. 9.
    Liu, H., Motoda, H.: Feature selection for knowledge discovery and data mining. Kluwer Academic Publishers, Dordrecht (1998)MATHGoogle Scholar
  10. 10.
    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
  11. 11.
    Mitchell, T.M.: Generalization as Search. Artificial Intelligence 18(2), 203–226 (1982)CrossRefMathSciNetGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    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
  14. 14.
    Ohsaki, M., Abe, H., Kitaguchi, S., Yokoi, H., Yamaguchi, T.: Development and Evaluation of A Time-Series Pattern Extraction Method Based on Irregular Sampling and Quantization. In: Proc. of the 69th workshop on knowledge base systems (SIG-KBS-A405-07), pp. 39–43 (in Japanese) (2005)Google Scholar
  15. 15.
    Quinlan, J.R.: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1992)Google Scholar
  16. 16.
    Tsumoto, S.: Hepatitis Dataset for Discovery Challenge (2002), http://lisp.vse.cz/challenge/ecmlpkdd2002/index.html
  17. 17.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hidenao Abe
    • 1
  • Miho Ohsaki
    • 2
  • Hideto Yokoi
    • 3
  • Takahira Yamaguchi
    • 4
  1. 1.Department of Medical InformaticsShimane University, School of Medicine 
  2. 2.Faculty of EngineeringDoshisha University 
  3. 3.Department of Medical InformaticsKagawa University Hospital 
  4. 4.Faculty of Science and TechnologyKeio University 

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