Advertisement

Temporal Rule Discovery for Time-Series Satellite Images and Integration with RDB

  • Rie Honda
  • Osamu Konishi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2168)

Abstract

Feature extraction and knowledge discovery from a large amount of image data such as remote sensing images have become highly required recent years. In this study, a framework for data mining from a set of time-series images including moving objects was presented. Time-series images are transformed into time-series cluster addresses by using clustering by two-stage SOM (Self-organizing map) and time-dependent association rules were extracted from it. Semantically indexed data and extracted rules are stored in the object-relational database, which allows high-level queries by entering SQL through the user interface. This method was applied to weather satellite cloud images taken by GMS-5 and its usefulness was evaluated.

References

  1. 1.
    Agrawal, R., Imelinski, T., Swani, A.: Mining in association rules between sets of items in large database. Proc. ACM SIGMOD International Conference (1993) 207–216Google Scholar
  2. 2.
    Agrawal, R. and Srikant, R.: Fast Algorithms for mining association rules. Proceedings of 20th International Conference on VLDB (1994) 487–499.Google Scholar
  3. 3.
    Alex, A.F., Simon, H.L.: Mining very large databases with parallel processing. Kluwer Academic Publishers (1998)Google Scholar
  4. 4.
    Burl, M.C., Asker, L., Smyth, P., Fayyad, U.M., Perona, P., Crumpler, L., Aubele, J.: Learning to recognize volcanos on Venus. Machine Learning, Vol. 30,(2/3) (1998) 165–195CrossRefGoogle Scholar
  5. 5.
    Fayyad, U.M., Djorgovski, S.G., Weir, N.: Automatic the analysis and cataloging of sky surveys. Advances in Knowledge Discovery and Data Mining, AAAI Press/MIT Press (1996) 471–493Google Scholar
  6. 6.
    Guttman, A.: R-trees: a dynamic index structure for spatial searching. Proc. ACM SIGMOD International Conference (1984) 47–57Google Scholar
  7. 7.
    Katayama, K., Konishi, O.: Construction satellite image databases for supporting knowledge discovery(in Japanese). Transaction of Information Processing Society of Japan, Vol. 40, SIG5(TOD2) (1999) 69–78Google Scholar
  8. 8.
    Katayama, K., Konishi, O.: Discovering co-occurencing patterns in event sequences (in Japanese). DEWS’99 (1999)Google Scholar
  9. 9.
    Kohonen, T.: Self-organizing maps. Springer (1995)Google Scholar
  10. 10.
    Konishi, O.: A statistically build knowledge based terminology construction system (in Japanese). Transaction of Information Processing Society of Japan, Vol. 30,2 (1989) 179–189Google Scholar
  11. 11.
    Mannila, H., H. Tovinen and A. I. Verkano. Discovering frequent episodes in sequences. In First International Conference on Knowledge Discovery and Data Mining(KDD’95), AAAI Press (1995) 210–215Google Scholar
  12. 12.
    Mannila, H., Tovinen, H.: Discovering generalized episodes using minimal occurrences. In Proceeding of the Second International Conference on Knowledge Discovery and Data Mining(KDD’96), AAAI Press (1996) 146–151Google Scholar
  13. 13.
    Merkl, D. Rauber, A.: Uncovering the hierarchical structure of text archives by using unsupervised neural network with adaptive architecture. In PAKDD 2000 (2000) 384–395Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Rie Honda
    • 1
  • Osamu Konishi
    • 1
  1. 1.Department of Mathematics and Information ScienceKochi UniversityJAPAN

Personalised recommendations