E-Stream: Evolution-Based Technique for Stream Clustering

  • Komkrit Udommanetanakit
  • Thanawin Rakthanmanon
  • Kitsana Waiyamai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4632)


Data streams have recently attracted attention for their applicability to numerous domains including credit fraud detection, network intrusion detection, and click streams. Stream clustering is a technique that performs cluster analysis of data streams that is able to monitor the results in real time. A data stream is continuously generated sequences of data for which the characteristics of the data evolve over time. A good stream clustering algorithm should recognize such evolution and yield a cluster model that conforms to the current data. In this paper, we propose a new technique for stream clustering which supports five evolutions that are appearance, disappearance, self-evolution, merge and split.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Milenova, B.L., Campos, M.M.: Clustering Large Databases with Numeric and Nominial Values Using Orthogonal Projections. In: Proceedings of the 29th VLDB Conference (2003)Google Scholar
  2. 2.
    Aggarwal, C., Han, J., Wang, J., Yu, P.S.: A Framework for Projected Clustering of High Dimensional Data Streams. In: Proceeding of the 30th VLDB conference (2004)Google Scholar
  3. 3.
    Aggarwal, C., Han, J., Wang, J., Yu, P.S.: A Framework for Clustering Evolving Data Streams. In: Proceeding of the 29th VLDB conference (2003)Google Scholar
  4. 4.
    Barbara, D.: Requirements for Clustering Data Streams. In: SIGKDD Explorations (2002)Google Scholar
  5. 5.
    Gaber, M.M., Zaslavsky, A., Krishnaswmy, S.: Mining Data Streams: A Review. SIGMOD Record 34(2) (June 2005)Google Scholar
  6. 6.
    Oh, S., Kang, J., Byun, Y., Park, G., Byun, S.: Intrusion Detection based on Clustering a Data Stream. In: Proceedings of the 2005 Third ACIS International Conference on Software Engineering Research, Management and Applications (2005)Google Scholar
  7. 7.
    Guha, S., Meyerson, A., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering Data Streams: Theory and Practice. TKDE special issue on clustering 15 (2003)Google Scholar
  8. 8.
    Song, M., Wang, H.: Highly Efficient Incremental Estimation of Gaussian Mixture Models for Online Data Stream Clustering. In: SPIE Conference on Intelligent Computing: Theory And Application III (2005)Google Scholar
  9. 9.
    Zhang, T., Ramakhrisnan, R., Livny, M.: BIRCH: An Efficient Data Clustering Method for Very Large Databases. In: Proc. ACM SIGMOD Int. Conf. Management of Data (1996)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Komkrit Udommanetanakit
    • 1
  • Thanawin Rakthanmanon
    • 1
  • Kitsana Waiyamai
    • 1
  1. 1.Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900Thailand

Personalised recommendations