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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2018: Advances in Knowledge Discovery and Data Mining pp 589–601Cite as

Anomaly Detection Technique Robust to Units and Scales of Measurement

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10937))

Abstract

Existing anomaly detection methods are sensitive to units and scales of measurement. Their performances vary significantly if feature values are measured in different units or scales. In many data mining applications, units and scales of feature values may not be known. This paper introduces a new anomaly detection technique using unsupervised stochastic forest, called ‘usfAD’, which is robust to units and scales of measurement. Empirical results show that it produces more consistent results than five state-of-the-art anomaly detection techniques across a wide range of synthetic and benchmark datasets.

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Author information

Authors and Affiliations

  1. School of Engineering and Information Technology, Federation University, Mount Helen, VIC, Australia

    Sunil Aryal

Authors
  1. Sunil Aryal
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Correspondence to Sunil Aryal .

Editor information

Editors and Affiliations

  1. Deakin University, Geelong, Victoria, Australia

    Dinh Phung

  2. National Chiao Tung University, Hsinchu City, Taiwan

    Vincent S. Tseng

  3. Monash University, Clayton, Victoria, Australia

    Geoffrey I. Webb

  4. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Bao Ho

  5. University of Melbourne, Melbourne, Victoria, Australia

    Mohadeseh Ganji

  6. University of Melbourne, Melbourne, Victoria, Australia

    Lida Rashidi

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Aryal, S. (2018). Anomaly Detection Technique Robust to Units and Scales of Measurement. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10937. Springer, Cham. https://doi.org/10.1007/978-3-319-93034-3_47

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  • DOI: https://doi.org/10.1007/978-3-319-93034-3_47

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93033-6

  • Online ISBN: 978-3-319-93034-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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