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Anomaly Detection Using Correctness Matching Through a Neighborhood Rough Set

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Book cover Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9949))

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Abstract

Abnormal information patterns are signals retrieved from a data source that could contain erroneous or reveal faulty behavior. Despite which signal it is, this abnormal information could affect the distribution of a real data. An anomaly detection method, i.e. Neighborhood Rough Set with Correctness Matching (NRSCM) is presented in this paper to identify abnormal information (outliers). Two real-life data sets, one mixed data and one categorical data, are used to demonstrate the performance of NRSCM. The experiments positively show good performance of NRSCM in detecting anomaly.

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Author information

Authors and Affiliations

  1. Multimedia University, Jln. Ayer Keroh Lama, 75450, Melaka, Malaysia

    Pey Yun Goh, Shing Chiang Tan & Wooi Ping Cheah

Authors
  1. Pey Yun Goh
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  2. Shing Chiang Tan
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  3. Wooi Ping Cheah
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Corresponding author

Correspondence to Pey Yun Goh .

Editor information

Editors and Affiliations

  1. The University of Tokyo , Tokyo, Japan

    Akira Hirose

  2. Kobe University , Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University , Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences , Beijing, China

    Derong Liu

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© 2016 Springer International Publishing AG

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Goh, P.Y., Tan, S.C., Cheah, W.P. (2016). Anomaly Detection Using Correctness Matching Through a Neighborhood Rough Set. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_47

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  • DOI: https://doi.org/10.1007/978-3-319-46675-0_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46674-3

  • Online ISBN: 978-3-319-46675-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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