Advertisement

Knowledge Management System: Combination of Experts’ Knowledge and Automatic Improvement

  • Hiroshi Sugimura
  • Kazunori Matsumoto
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 156)

Abstract

This paper proposes a knowledge management system that acquires knowledge from time series data by using users background knowledge. To obtain if-then rules as knowledge, we apply decision tree learning. However usual methods of decision tree learning targets discrete value data, thus a new approach is needed for dealing with this type of data. Experts forecast future events by using their knowledge. They, in typical cases, focus on a set of useful patterns and then apply knowledge relevant to them. We apply this idea into the framework of decision tree learning. We prepare a set of patterns, which is called clues, and then express time series data in terms of the clues. Thus the clues are attributes by which features of data are described. In addition, to make a better prediction with the learning process, we develop a mechanism that improves the quality of the clues. The essential idea of the mechanism is based on a genetic algorithm. The clue is evaluated by using entropy of information theory, and is improved by GA operators. We can obtain new knowledge from improved clues and the extracted decision tree. This paper details the system and results of the experiment.

Keywords

Time Series Data Dynamic Time Warping Future Behavior Roulette Wheel Selection Knowledge Management System 
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.
    Dong, G., Pei, J.: Sequence Data Mining. Springer (2007)Google Scholar
  2. 2.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Pub. (2005)Google Scholar
  3. 3.
    Pješivac-Grbović, J., Bosilca, G., Fagg, G.E., Angskun, T., Dongarra, J.: Decision trees and MPI collective algorithm selection problem. In: Kermarrec, A.-M., Bougé, L., Priol, T. (eds.) Euro-Par 2007. LNCS, vol. 4641, pp. 107–117. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Kubota, K., Nakase, A., Sakai, H., Oyanagi, S.: Parallelization of decision tree algorithm and its performance evaluation. IPSJ SIG. Notes 99(66), 161–166 (1999)Google Scholar
  5. 5.
    Yamada, Y., Suzuki, E., Yokoi, H., Takabayashi, K.: Decision-tree induction from time-series data based on a standard-example split test. In: Proceedings of the Twentieth International Conference on Machine Learning (ICML), pp. 840–847 (2003)Google Scholar
  6. 6.
    Abe, H., Hirabayashi, S., Ohsaki, M., Yamaguchi, T.: Evaluating a trading rule mining method based on temporal pattern extraction. In: The Third International Workshop on Mining Complex Data (MCD 2007) In Conjunction with ECML/PKDD 2007, pp. 49–58 (2007)Google Scholar
  7. 7.
    Keogh, E., Lin, J.: Clustering of time-series subsequences is meaningless: implications for previous and future research. Knowledge and Information Systems 8(2), 154–177 (2005)CrossRefGoogle Scholar
  8. 8.
    Warren Liao, T.: Clustering of time series data–a survey. Pattern Recognition 38(11), 1857–1874 (2005)zbMATHCrossRefGoogle Scholar
  9. 9.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)Google Scholar
  10. 10.
    Cha, S.-H., Tappert, C.: A genetic algorithm for constructing compact binary decision trees. Journal of Pattern Recognition Research (JPRR) 4(1), 1–13 (2009)Google Scholar
  11. 11.
    Berndt, D.J., Clifford, J.: Using Dynamic Time Warping to Find Patterns in Time Series. In: Proceedings of KDD 1994: AAAI Workshop on Knowledge Discovery in Databases, Seattle, Washington, pp. 359–370 (July 1994)Google Scholar
  12. 12.
    Ross Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers (1993)Google Scholar
  13. 13.
    The University of Waikato, Machine learning project at the university of waikato in new zealand, http://www.cs.waikato.ac.nz/ml/
  14. 14.
    NESDIS-National Environmental Satellite, Data, and Information Service, Nndc climate data online, http://www.nesdis.noaa.gov/

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Kanagawa Institute of TechnologyAtsugi-shiJapan

Personalised recommendations