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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)

Abstract

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.

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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

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