Data Streaming with Affinity Propagation

  • Xiangliang Zhang
  • Cyril Furtlehner
  • Michèle Sebag
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5212)


This paper proposed StrAP (Streaming AP), extending Affinity Propagation (AP) to data steaming. AP, a new clustering algorithm, extracts the data items, or exemplars, that best represent the dataset using a message passing method. Several steps are made to build StrAP. The first one (Weighted AP) extends AP to weighted items with no loss of generality. The second one (Hierarchical WAP) is concerned with reducing the quadratic AP complexity, by applying AP on data subsets and further applying Weighted AP on the exemplars extracted from all subsets. Finally StrAP extends Hierarchical WAP to deal with changes in the data distribution. Experiments on artificial datasets, on the Intrusion Detection benchmark (KDD99) and on a real-world problem, clustering the stream of jobs submitted to the EGEE grid system, provide a comparative validation of the approach.


Data Stream Intrusion Detection Change Point Detection Stream Model Very Large Data Base 
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

  • Xiangliang Zhang
    • 1
  • Cyril Furtlehner
    • 1
  • Michèle Sebag
    • 1
  1. 1.TAO − INRIA CNRSOrsay CedexFrance

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