Advertisement

Efficient Mining of Emerging Events in a Dynamic Spatiotemporal Environment

  • Yu Meng
  • Margaret H. Dunham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)

Abstract

This paper presents an efficient data mining technique for modeling multidimensional time variant data series and its suitability for mining emerging events in a spatiotemporal environment. The data is modeled using a data structure that interleaves a clustering method with a dynamic Markov chain. Novel operations are used for deleting obsolete states, and finding emerging events based on a scoring scheme. The model is incremental, scalable, adaptive, and suitable for online processing. Algorithm analysis and experiments demonstrate the efficiency and effectiveness of the proposed technique.

Keywords

Markov Chain Model Concept Drift Cluster Feature Efficient Mining Time Series Database 
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.
    Dunham, M., Meng, Y., Huang, J.: Extensible Markov Model. In: ICDM, pp. 371–374 (2004)Google Scholar
  2. 2.
    Aggarwal, H., et al.: A Framework for Clustering Evolving Data Streams. In: VLDB 2003 (2003)Google Scholar
  3. 3.
    Pratt, K.B., Tschapek, G.: Visualizing Concept Drift. In: SIGKDD 2003 (2003)Google Scholar
  4. 4.
    Ye, N., Li, X.: A Markov Chain Model of Temporal Behavior for Anomaly Detection. In: Proc. IEEE Systems, Man, and Cybernetics Information Assurance and Security Workshop (2000)Google Scholar
  5. 5.
    Keogh, E., et al.: Finding Surprising Patterns in a Time Series Database in Linear Time and Space. In: SIGKDD 2002, pp. 550–556 (2002)Google Scholar
  6. 6.
    Yu, D., Sheikholeslami, G., Zhang, A.: FindOut: Finding Outliers in Very Large Datasets. Knowledge and Information Systems 4(4), 387–412 (2002)CrossRefGoogle Scholar
  7. 7.
    Weiss, G.M., Hirsh, H.: Learning to Predict Extremely Rare Events. In: AAAI Workshop Learning from Imbalanced Data Sets, pp. 64–68 (2000)Google Scholar
  8. 8.
    Domingos, P., Hulten, G.: Mining High-speed Data Streams. Knowledge Discovery and Data Mining, 71–80 (2000)Google Scholar
  9. 9.
    Widmer, G., Kubat, M.: Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning 23, 69–101 (1996)Google Scholar
  10. 10.
    Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: A New Data Clustering Algorithm and Its Applications. Data Mining and Knowledge Discovery 1(2), 141–182 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yu Meng
    • 1
  • Margaret H. Dunham
    • 1
  1. 1.Department of Computer Science and EngineeringSouthern Methodist UniversityDallasUSA

Personalised recommendations