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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)

Abstract

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.

Keywords

Outlier Detection Synchronization Kuramoto model 

References

  1. 1.
    Hawkins, D.: Identification of Outliers. Chapman and Hall, London (1980)zbMATHGoogle Scholar
  2. 2.
    Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. In: Proceedings of the ACM SIGMOD Conference (2000)Google Scholar
  3. 3.
    Knorr, E.M., Ng, R.T.: Algorithms for mining distance-based outliers in large datasets. In: VLDB, pp. 392–403 (1998)Google Scholar
  4. 4.
    Knorr, E.M., Ng, R.T.: Finding intensional knowledge of distance-based outliers. In: VLDB, pp. 211–222 (1999)Google Scholar
  5. 5.
    Boehm, C., Haegler, K., Mueller, N.S., Plant, C.: CoCo: Coding Cost For Parameter-Free Outlier Detection. In: Proc. ACM SIGKDD 2009, pp. 149–158 (2009)Google Scholar
  6. 6.
    Arenas, J.K.Y.M.A., Guilera, A.D., Zhou, C.S.: Synchronization in complex networks. Phys. Rep. 469, 93–1535 (2008)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Barnett, V., Lewis, T.: Outliers in Statistical Data. John Wiley, Chichester (1994)zbMATHGoogle Scholar
  8. 8.
    Rousseeuw, P.J., Leroy, A.M.: Robust Regression and Outlier Detection. John Wiley and Sons, Chichester (1987)zbMATHCrossRefGoogle Scholar
  9. 9.
    Yamanishi, K., Takeuchi, J., Williams, G., Milne, P.: On-line unsupervised outlier detection using finite mixtures with discounting learning algorithm. In: Proceedings of KDD 2000, pp. 320–324 (2000)Google Scholar
  10. 10.
    Yamanishi, K., Takeuchi, J.: Discovering Outlier Filtering Rules from Unlabeled Data. In: Proc. ACM SIGKDD 2001, pp. 389–394 (2001)Google Scholar
  11. 11.
    Ramaswamy, S., Rastogi, R., Kyuseok, S.: Efficient Algorithms for Mining Outliers from Large Data Sets. In: Proc. ACM SIDMOD Int. Conf. on Management of Data (2000)Google Scholar
  12. 12.
    Tang, J., Chen, Z., Fu, A.W.-C., Cheung, D.W.: Enhancing effectiveness of outlier detections for low density patterns. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, p. 535. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Papadimitriou, S., Kitagawa, H., Gibbons, P.B., Faloutsos, C.: LOCI: Fast Outlier Detection Using the Local Correlation Integral. In: Proceedings of IEEE International Conference on Data engineering, Bangalore, India (2003)Google Scholar
  14. 14.
    Kuramoto, Y.: In: Araki, H. (ed.) Proceedings of the International Symposium on Mathematical Problems in Theoretical Physics. Lecture Notes in Physics, pp. 420–422. Springer, New York (1975)CrossRefGoogle Scholar
  15. 15.
    Kuramoto, Y.: Chemical oscillations, waves, and turbulence. Springer, New York (1984)zbMATHGoogle Scholar
  16. 16.
    Arenas, A., Diaz-Guilera, A., Perez-Vicente, C.J.: Plasticity and learning in a network of coupled phase oscillators. Phys. Rev. Lett. 96 (2006)Google Scholar
  17. 17.
    Kim, C.S., Bae, C.S., Tcha, H.J.: A phase synchronization clustering algorithm for identifying interesting groups of genes from cell cycle expression data. BMC Bioinformatics 9(56) (2008)Google Scholar
  18. 18.
    Böhm, C., Plant, C., Shao, J., Yang, Q.: Clustering by Synchronization. In: Proc. of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA (2010)Google Scholar
  19. 19.
    Aeyels, D., Smet, F.D.: A mathematical model for the dynamics of clustering. Physica D: Nonlinear Phenomena 273(19), 2517–2530 (2008)CrossRefGoogle Scholar

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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