Synchronization Based Outlier Detection

  • Junming Shao
  • Christian Böhm
  • Qinli Yang
  • Claudia Plant
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6323)


The study of extraordinary observations is of great interest in a large variety of applications, such as criminal activities detection, athlete performance analysis, and rare events or exceptions identification. The question is: how can we naturally flag these outliers in a real complex data set? In this paper, we study outlier detection based on a novel powerful concept: synchronization. The basic idea is to regard each data object as a phase oscillator and simulate its dynamical behavior over time according to an extensive Kuramoto model. During the process towards synchronization, regular objects and outliers exhibit different interaction patterns. Outlier objects are naturally detected by local synchronization factor (LSF). An extensive experimental evaluation on synthetic and real world data demonstrates the benefits of our method.


Outlier Detection Synchronization Kuramoto model 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Junming Shao
    • 1
  • Christian Böhm
    • 1
  • Qinli Yang
    • 2
  • Claudia Plant
    • 3
  1. 1.Institute of Computer ScienceUniversity of MunichGermany
  2. 2.School of EngineeringUniversity of EdinburghUK
  3. 3.Department of Scientific ComputingFlorida State UniversityUSA

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