A Nonparametric Outlier Detection for Effectively Discovering Top-N Outliers from Engineering Data

  • Hongqin Fan
  • Osmar R. Zaïane
  • Andrew Foss
  • Junfeng Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)


We present a novel resolution-based outlier notion and a nonparametric outlier-mining algorithm, which can efficiently identify top listed outliers from a wide variety of datasets. The algorithm generates reasonable outlier results by taking both local and global features of a dataset into consideration. Experiments are conducted using both synthetic datasets and a real life construction equipment dataset from a large building contractor. Comparison with the current outlier mining algorithms indicates that the proposed algorithm is more effective.


Outlier Detection Mining Algorithm Close Neighbour Synthetic Dataset Local Outlier 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hongqin Fan
    • 1
  • Osmar R. Zaïane
    • 2
  • Andrew Foss
    • 2
  • Junfeng Wu
    • 2
  1. 1.Department of Civil EngineeringUniversity of AlbertaCanada
  2. 2.Department of Computing ScienceUniversity of AlbertaCanada

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