Data Intensive vs Sliding Window Outlier Detection in the Stream Data — An Experimental Approach

  • Mateusz Kalisch
  • Marcin Michalak
  • Marek Sikora
  • Łukasz Wróbel
  • Piotr Przystałka
Conference paper

DOI: 10.1007/978-3-319-39384-1_7

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9693)
Cite this paper as:
Kalisch M., Michalak M., Sikora M., Wróbel Ł., Przystałka P. (2016) Data Intensive vs Sliding Window Outlier Detection in the Stream Data — An Experimental Approach. In: Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L., Zurada J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science, vol 9693. Springer, Cham

Abstract

In the paper a problem of outlier detection in the stream data is raised. The authors propose a new approach, using well known outlier detection algorithms, of outlier detection in the stream data. The method is based on the definition of a sliding window, which means a sequence of stream data observations from the past that are closest to the newly coming object. As it may be expected the outlier detection accuracy level of this model becomes worse than the accuracy of the model that uses all historical data, but from the statistical point of view the difference is not significant. In the paper several well known methods of outlier detection are used as the basis of the model.

Keywords

Outlier detection Data analysis Classification Time series 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mateusz Kalisch
    • 1
  • Marcin Michalak
    • 2
  • Marek Sikora
    • 2
    • 3
  • Łukasz Wróbel
    • 3
  • Piotr Przystałka
    • 1
  1. 1.Institute of Fundamentals of Machinery DesignSilesian University of TechnologyGliwicePoland
  2. 2.Institute of InformaticsSilesian University of TechnologyGliwicePoland
  3. 3.Institute of Innovative Technologies EMAGKatowicePoland

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