Connectivity Based Stream Clustering Using Localised Density Exemplars

  • Sebastian Lühr
  • Mihai Lazarescu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5012)


Advances in data acquisition have allowed large data collections of millions of time varying records in the form of data streams. The challenge is to effectively process the stream data with limited resources while maintaining sufficient historical information to define the changes and patterns over time. This paper describes an evidence-based approach that uses representative points to incrementally process stream data by using a graph based method to cluster points based on connectivity and density. Critical cluster features are archived in repositories to allow the algorithm to cope with recurrent information and to provide a rich history of relevant cluster changes if analysis of past data is required. We demonstrate our work with both synthetic and real world data sets.


Execution Time Data Stream Very Large Scale Integration Sparse Graph Binary Search Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Sebastian Lühr
    • 1
  • Mihai Lazarescu
    • 1
  1. 1.Department of ComputingCurtin University of TechnologyBentley 

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