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Efficiently Clustering Probabilistic Data Streams

  • Chen Zhang
  • Cheqing Jin
  • Aoying Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5446)

Abstract

Data mining on uncertain data stream has attracted a lot of attentions because of the widely existed imprecise data generated from a variety of streaming applications in recent years. The main challenge of mining uncertain data streams stems from the strict space and time requirements of processing arriving tuples in high-speed. When new tuples arrive, the number of the possible world instances will increase exponentially related to the volume of the data stream. As one of the most important mining task, how to devise clustering algorithms has been studied intensively on deterministic data streams, whereas the work on the uncertain data streams still remains rare. This paper proposes a novel solution for clustering on uncertain data streams in point probability model, where the existence of each tuple is uncertain. Detailed analysis and the thorough experimental reports both on synthetic and real data sets illustrate the advantages of our new method in terms of effectiveness and efficiency.

Keywords

Uncertain data stream Clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Chen Zhang
    • 1
  • Cheqing Jin
    • 2
    • 3
  • Aoying Zhou
    • 2
    • 3
  1. 1.Department of Computer Science and EngineeringFudan UniversityP.R. China
  2. 2.Software Engineering Institute of East China Normal UniversityP.R. China
  3. 3.Shanghai Key Laboratory of Trustworthy ComputeringP.R. China

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