Tracking Drift Types in Changing Data Streams

  • David T. J. Huang
  • Yun Sing Koh
  • Gillian Dobbie
  • Russel Pears
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8346)


The rate of change of drift in a data stream can be of interest. It could show, for example, that a strand of bacteria is becoming more resistant to a drug, or that a machine is becoming unreliable and requires maintenance. While concept drift in data streams has been widely studied, no one has studied the rate of change in concept drift. In this paper we define three new drift types: relative abrupt drift, relative moderate drift and relative gradual drift. We propose a novel algorithm that tracks changes in drift intensity relative to previous drift points within the stream. The algorithm is based on mapping drift patterns to a Gaussian function. Our experimental results show that the algorithm is robust and achieving accuracy levels above 90%.


Data Stream Relative Drift Types Gaussian Curve 


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  1. 1.
    Baena-García, M., del Campo-Ávila, J., Fidalgo, R., Bifet, A., Gavaldá, R., Morales-Bueno, R.: Early drift detection method. In: Fourth International Workshop on Knowledge Discovery from Data Streams (2006)Google Scholar
  2. 2.
    Bartlett, P., Ben-David, S., Kulkarni, S.: Learning changing concepts by exploiting the structure of change. Machine Learning 41(2), 153–174 (2000)CrossRefzbMATHGoogle Scholar
  3. 3.
    Bifet, A., Gavaldá, R.: Learning from time-changing data with adaptive windowing. In: SIAM International Conference on Data Mining (2007)Google Scholar
  4. 4.
    Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286–295. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Hoeffding, W.: Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association 58, 13–29 (1963)CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    Kifer, D., Ben-David, S., Gehrke, J.: Detecting change in data streams. In: Proceedings of the Thirtieth International Conference on VLDB, vol. 30, pp. 180–191. VLDB Endowment (2004)Google Scholar
  7. 7.
    Kosina, P., Gama, J., Sebastião, R.: Drift severity metric. In: Proceedings of the 2010 Conference on ECAI 2010: 19th European Conference on Artificial Intelligence, pp. 1119–1120. IOS Press, Amsterdam (2010)Google Scholar
  8. 8.
    Sebastião, R., Gama, J.: A study on change detection methods. In: 4th Portuguese Conf. on Artificial Intelligence, Lisbon (2009)Google Scholar
  9. 9.
    Vitter, J.S.: Random sampling with a reservoir. ACM Trans. Math. Softw. 11(1), 37–57 (1985)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Zhang, P., Zhu, X., Shi, Y.: Categorizing and mining concept drifting data streams. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 812–820. ACM, New York (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • David T. J. Huang
    • 1
  • Yun Sing Koh
    • 1
  • Gillian Dobbie
    • 1
  • Russel Pears
    • 2
  1. 1.Department of Computer ScienceUniversity of AucklandNew Zealand
  2. 2.School of Computing and Mathematical SciencesAUT UniversityNew Zealand

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