Fourier Transform Based Spatial Outlier Mining

  • Faraz Rasheed
  • Peter Peng
  • Reda Alhajj
  • Jon Rokne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)


Outlier detection is an important problem in spatial analysis which involves finding a region of spatial locations with features significantly different from the rest of the population. In this paper, we used fast fourier transform to highlight the areas with high frequency change. The spatial points identified by the fourier transform are then reconfirmed with Z-value test and outlier regions are identified. We performed several experiments to highlight the accuracy and efficiency of the approach and compared it with some other existing approaches.


Spatial analysis outlier detection fourier transform curve fitting 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bloomfield, P.: Fourier Analysis of Time Series - An Introduction. John Wiley, New York (2000)CrossRefzbMATHGoogle Scholar
  2. 2.
    Barua, S., Alhajj, R.: A Parallel Multi-scale Region Outlier Mining Algorithm For Meteorological Data. In: Proc. of the 15th Annual ACM international Symposium on Advances in Geographic information Systems (2003)Google Scholar
  3. 3.
    Birant, D., Kut, A.: Spatio-temporal outlier detection in large databases. Journal of Computing and Information Technology 14(4), 291–298 (2006)CrossRefGoogle Scholar
  4. 4.
    Cheng, T., Li, Z.: A multiscale approach for spatio-temporal outlier detection. Transactions in GIS 10(2), 253–263 (2006)CrossRefGoogle Scholar
  5. 5.
    Edwin, M.K., Raymond, T.N.: A unified notion of outliers: Properties and computation. In: Proc. of ACM-KDD, Newport Beach, CA, pp. 219–222 (1997)Google Scholar
  6. 6.
    Ramaswamy, S., Alto, P., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large datasets. In: Proc. of ACM SIGMOD (2000)Google Scholar
  7. 7.
    Shekhar, S., Lu, C.T., Zhang, P.: Detecting graph-based spatial outliers: algorithms and applications (a summary of results). In: Proc. of the seventh ACM SIGKDD, pp. 371–376 (2001)Google Scholar
  8. 8.
    Shekhar, S., Lu, C.-T., Zhang, P.: A unified approach to detecting spatial outliers. GeoInformatica 7(2) (2003)Google Scholar
  9. 9.
    Luc, A.: Local indicators of spatial association: Lisa. Geographical Analysis 27(2), 93–115 (1995)Google Scholar
  10. 10.
    Lu, C., Kou, Y., Zhao, J., Chen, L.: Detecting and Tracking Regional Outliers in Meteorological Data. Information Science 177(7), 1609–1632 (2007)CrossRefGoogle Scholar
  11. 11.
    Zhao, J., Lu, C., Kou, Y.: Detecting region outliers in meteorological data. In: Proc. of ACM International Symposium on Advances in GIS, pp. 49–55 (2003)Google Scholar
  12. 12.
  13. 13.
    Shittu, O.I., Shangodoyin, D.K.: Detection of Outliers in Time Series Data: A Frequency Domain Approach. Asian Journal of Scientific Research 1(2), 130–137 (2008)CrossRefGoogle Scholar
  14. 14.
    Sun, P., Chawla, S.: On local spatial outliers. In: Proc. of IEEE ICDM, pp. 209–216 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Faraz Rasheed
    • 1
  • Peter Peng
    • 1
  • Reda Alhajj
    • 1
    • 2
  • Jon Rokne
    • 1
  1. 1.Dept. of Computer ScienceUniversity of CalgaryCalgaryCanada
  2. 2.Dept. of Computer ScienceGlobal UniversityBeirutLebanon

Personalised recommendations