Advertisement

Active Mining pp 112-125 | Cite as

First-Order Rule Mining by Using Graphs Created from Temporal Medical Data

  • Ryutaro Ichise
  • Masayuki Numao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3430)

Abstract

In managing medical data, handling time-series data, which contain irregularities, presents the greatest difficulty. In the present paper, we propose a first-order rule discovery method for handling such data. The present method is an attempt to use graph structure to represent time-series data and reduce the graph using specified rules for inducing hypothesis. In order to evaluate the proposed method, we conducted experiments using real-world medical data.

Keywords

Medical Data Dynamic Time Warping Interferon Therapy Inductive Logic Programming Horn Clause 
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.
    Adriaans, P., Zantinge, D.: Data Mining. Addison Wesley, London (1996)Google Scholar
  2. 2.
    Baxter, R., Williams, G., He, H.: Feature Selection for Temporal Health Records. In: Cheung, D., Williams, G.J., Li, Q. (eds.) PAKDD 2001. LNCS (LNAI), vol. 2035, pp. 198–209. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Das, D., Lin, K., Mannila, H., Renganathan, G., Smyth, P.: Rule Discovery from Time Series. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pp. 16–22 (1998)Google Scholar
  4. 4.
    Džeroski, S., Lavrač, N.: Relational Data Mining. Springer, Heidelberg (2001)MATHGoogle Scholar
  5. 5.
    Gamberger, D., Lavrač, N., Krstačić, G.: Active subgroup mining: a case study in coronary heart disease risk group detection. Artificial Intelligence in Medicine 28, 27–57 (2003)CrossRefGoogle Scholar
  6. 6.
    Ichise, R., Numao, M.: Learning first-order rules to handle medical data. NII Journal 2, 9–14 (2001)Google Scholar
  7. 7.
    Keogh, E., Pazzani, M.: Scaling up Dynamic Time Warping for Datamining Applications. In: The Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining, pp. 285–289 (2000)Google Scholar
  8. 8.
    Kononenko, I.: Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in Medicine 23, 89–109 (2001)CrossRefGoogle Scholar
  9. 9.
    Motoda, H. (ed.): Active mining: new directions of data mining. Frontiers in artificial intelligence and applications, vol. 79. IOS Press, Amsterdam (2002)MATHGoogle Scholar
  10. 10.
    Muggleton, S., Firth, J.: Relational rule induction with CProgol4.4: a tutorial introduction. In: Relational Data Mining, pp. 160–188 (2001)Google Scholar
  11. 11.
    Quinlan, J.R.: Learning logical definitions from relation. Machine Learning 5(3), 239–266 (1990)Google Scholar
  12. 12.
    Rodríguez, J.J., Alonso, C.J., Bostrø"m, H.: Learning First Order Logic Time Series Classifiers: Rules and Boosting. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 299–308. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  13. 13.
    Spenke, M.: Visualization and interactive analysis of blood parameters with InfoZoom. Artificial Intelligence in Medicine 22, 159–172 (2001)CrossRefGoogle Scholar
  14. 14.
    Tsumoto, S.: Rule Discovery in Large Time-Series Medical Databases. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 23–31. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  15. 15.
    Yamada, Y., Suzuki, E., Yokoi, H., Takabayashi, K.: Classification by Time-series Decision Tree. In: Proceedings of the 17th Annual Conference of the Japanese Society for Artificial Intelligence (2003) (in Japanese, 1F5-06)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ryutaro Ichise
    • 1
  • Masayuki Numao
    • 2
  1. 1.Intelligent Systems Research DivisionNational Institute of InformaticsTokyoJapan
  2. 2.The Institute of Scientific and Industrial ResearchOsaka UniversityOsakaJapan

Personalised recommendations