Data Streaming with Affinity Propagation

  • Xiangliang Zhang
  • Cyril Furtlehner
  • Michèle Sebag
Conference paper

DOI: 10.1007/978-3-540-87481-2_41

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5212)
Cite this paper as:
Zhang X., Furtlehner C., Sebag M. (2008) Data Streaming with Affinity Propagation. In: Daelemans W., Goethals B., Morik K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2008. Lecture Notes in Computer Science, vol 5212. Springer, Berlin, Heidelberg

Abstract

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.

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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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