Frequent Events and Epochs in Data Stream

  • Krzysztof Cabaj
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4585)


Currently used data-mining algorithms treat data globally. Nevertheless, with such methods, potentially useful knowledge that relates to local phenomena may be undetected. In this paper, we introduce new patterns in a form of local frequent events and epochs, boundaries of which correspond to discovered changes in a data stream. A local frequent event is an event which occurs in some period of time frequently, but not necessarily in the whole data stream. Such an event will be called a frequent event in a data stream. An epoch is understood as a sufficiently large group of frequent events that occur in a similar part of the data stream. The epochs are defined in such a way that they do not overlap are separated by so called change periods. In the paper, we discuss some potential applications of the proposed knowledge. Preliminary experiments are described as well.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB’94. Proc. Int. Conf. Very Large Data Bases, Santiago, Chile, September 1994, pp. 487–499 (1994)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: ICDE’95. Proc. Int. Conf. Data Engineering, Taipei, Taiwan, March 1995, pp. 3–14 (1995)Google Scholar
  3. 3.
    Dong, G., Li, J.: Efficient mining of emerging patterns: Discovering trends and differences. In: SIGKDD’99. Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, San Diego, pp. 43–52 (1999)Google Scholar
  4. 4.
    Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  5. 5.
    Pei, J., Han, J., in, i: PrefixSpan: Mining Sequential Patterns Efficiently by Prefix Projected Pattern Growth. In: ICDE’01. Proc. 2001 Int. Conf. Data Engineering, Heidelberg, Germany, April 2001, pp. 215–224 (2001)Google Scholar
  6. 6.
    Mannila, H., Toivonen, H., Verkamo, A.I.: Discovering frequent episodes in sequence. In: Proceedings of the First International Conference on Knowledge Discovery and Data Mining. Montreal, Quebec, pp. 144–155 (1995)Google Scholar
  7. 7.
    Klemettinen, M.: A Knowledge Discovery Methods for Telecommunication Network Alarm Database, PhD. Thesis, University of Helsinki (January 1999)Google Scholar
  8. 8.
    NASA: NASA’s Exploration Systems Architecture Study – Final Report, Part 5: Crew Exploration Vehicle,
  9. 9.

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Krzysztof Cabaj
    • 1
  1. 1.Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665 WarsawPoland

Personalised recommendations