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An Incremental Data Stream Clustering Algorithm Based on Dense Units Detection

  • Jing Gao
  • Jianzhong Li
  • Zhaogong Zhang
  • Pang-Ning Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3518)

Abstract

The data stream model of computation is often used for analyzing huge volumes of continuously arriving data. In this paper, we present a novel algorithm called DUCstream for clustering data streams. Our work is motivated by the needs to develop a single-pass algorithm that is capable of detecting evolving clusters, and yet requires little memory and computation time. To that end, we propose an incremental clustering method based on dense units detection. Evolving clusters are identified on the basis of the dense units, which contain relatively large number of points. For efficiency reasons, a bitwise dense unit representation is introduced. Our experimental results demonstrate DUCstream’s efficiency and efficacy.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jing Gao
    • 1
  • Jianzhong Li
    • 2
  • Zhaogong Zhang
    • 2
  • Pang-Ning Tan
    • 1
  1. 1.Dept. of Computer Science & EngineeringMichigan State UniversityEast LansingUSA
  2. 2.Dept. of Computer Science & TechnologyHarbin Institute of TechnologyHarbinChina

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