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On the Correlation Between Local Intrinsic Dimensionality and Outlierness

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Similarity Search and Applications (SISAP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11223))

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Abstract

Data mining methods for outlier detection are usually based on non-parametric density estimates in various variations. Here we argue for the use of local intrinsic dimensionality as a measure of outlierness and demonstrate empirically that it is a meaningful alternative and complement to classic methods.

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Notes

  1. 1.

    http://www.dbs.ifi.lmu.de/research/outlier-evaluation.

  2. 2.

    http://www.dbs.ifi.lmu.de/research/outlier-evaluation.

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Acknowledgments

M. E. Houle supported by JSPS Kakenhi Kiban (B) Research Grant 18H03296.

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Authors and Affiliations

  1. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan

    Michael E. Houle

  2. Heidelberg University, Im Neuenheimer Feld 205, 69120, Heidelberg, Germany

    Erich Schubert

  3. University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark

    Arthur Zimek

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  1. Michael E. Houle
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  2. Erich Schubert
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  3. Arthur Zimek
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Correspondence to Erich Schubert .

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  1. University of Geneva, Carouge, Switzerland

    Stéphane Marchand-Maillet

  2. Arizona State University, Tempe, AZ, USA

    Yasin N. Silva

  3. Center for Scientific Research and Higher Education, Ensenada, Mexico

    Edgar Chávez

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Houle, M.E., Schubert, E., Zimek, A. (2018). On the Correlation Between Local Intrinsic Dimensionality and Outlierness. In: Marchand-Maillet, S., Silva, Y., Chávez, E. (eds) Similarity Search and Applications. SISAP 2018. Lecture Notes in Computer Science(), vol 11223. Springer, Cham. https://doi.org/10.1007/978-3-030-02224-2_14

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