Temporal Representation in Spike Detection of Sparse Personal Identity Streams

  • Clifton Phua
  • Vincent Lee
  • Ross Gayler
  • Kate Smith
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3917)


Identity crime has increased enormously over the recent years. Spike detection is important because it highlights sudden and sharp rises in intensity relative to the current identity attribute value (which can be indicative of abuse). This paper proposes the new spike analysis framework for monitoring sparse personal identity streams. For each identity example, it detects spikes in single attribute values and integrates multiple spikes from different attributes to produce a numeric suspicion score. Although only temporal representation is examined here, experimental results on synthetic and real credit applications reveal some conditions on which the framework will perform well.


Discrete Wavelet Transform Synthetic Data Exponentially Weight Move Average Temporal Representation Stream Mining 
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 2006

Authors and Affiliations

  • Clifton Phua
    • 1
  • Vincent Lee
    • 1
  • Ross Gayler
    • 2
  • Kate Smith
    • 1
  1. 1.Clayton School of Information TechnologyMonash UniversityMelbourneAustralia
  2. 2.Baycorp AdvantageMelbourneAustralia

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