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Mining High Impact Exceptional Behavior Patterns

  • Longbing Cao
  • Yanchang Zhao
  • Fernando Figueiredo
  • Yuming Ou
  • Dan Luo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4819)

Abstract

In the real world, exceptional behavior can be seen in many situations such as security-oriented fields. Such behavior is rare and dispersed, while some of them may be associated with significant impact on the society. A typical example is the event September 11. The key feature of the above rare but significant behavior is its high potential to be linked with some significant impact. Identifying such particular behavior before generating impact on the world is very important. In this paper, we develop several types of high impact exceptional behavior patterns. The patterns include frequent behavior patterns which are associated with either positive or negative impact, and frequent behavior patterns that lead to both positive and negative impact. Our experiments in mining debt-associated customer behavior in social-security areas show the above approaches are useful in identifying exceptional behavior to deeply understand customer behavior and streamline business process.

Keywords

Frequent Itemsets Relative Risk Ratio Slide Time Window Exceptional Behavior Window Mode 
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 2007

Authors and Affiliations

  • Longbing Cao
    • 1
  • Yanchang Zhao
    • 1
  • Fernando Figueiredo
    • 2
  • Yuming Ou
    • 1
  • Dan Luo
    • 1
  1. 1.Faculty of Information Technology, University of Technology, SydneyAustralia
  2. 2.CentrelinkAustralia

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