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Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection

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Abstract

Outlier detection research has been seeing many new algorithms every year that often appear to be only slightly different from existing methods along with some experiments that show them to “clearly outperform” the others. However, few approaches come along with a clear analysis of existing methods and a solid theoretical differentiation. Here, we provide a formalized method of analysis to allow for a theoretical comparison and generalization of many existing methods. Our unified view improves understanding of the shared properties and of the differences of outlier detection models. By abstracting the notion of locality from the classic distance-based notion, our framework facilitates the construction of abstract methods for many special data types that are usually handled with specialized algorithms. In particular, spatial neighborhood can be seen as a special case of locality. Here we therefore compare and generalize approaches to spatial outlier detection in a detailed manner. We also discuss temporal data like video streams, or graph data such as community networks. Since we reproduce results of specialized approaches with our general framework, and even improve upon them, our framework provides reasonable baselines to evaluate the true merits of specialized approaches. At the same time, seeing spatial outlier detection as a special case of local outlier detection, opens up new potentials for analysis and advancement of methods.

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Notes

  1. This adaptation could also be considered “Schönfinkeling” (or, tastier, “Currying”) of the original DB-outlier model.

  2. Available at the US Census Bureau, United States Department of Commerce. http://www.census.gov/.

  3. Available at the Arizona State University GeoDa Center, http://geodacenter.asu.edu/.

  4. Available at Statistisches Bundesamt Deutschland, http://www.destatis.de.

  5. http://www.dblp.org/db/.

  6. Xapian search engine library, http://xapian.org/, GPL.

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Acknowledgments

Arthur Zimek is partially supported by NSERC.

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Authors and Affiliations

  1. Ludwig-Maximilians-Universität München, Oettingenstr. 67, 80538 , Munich, Germany

    Erich Schubert & Hans-Peter Kriegel

  2. Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada

    Arthur Zimek

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  1. Erich Schubert
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  2. Arthur Zimek
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  3. Hans-Peter Kriegel
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Correspondence to Arthur Zimek.

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Responsible editor: M. J. Zaki.

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Schubert, E., Zimek, A. & Kriegel, HP. Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection. Data Min Knowl Disc 28, 190–237 (2014). https://doi.org/10.1007/s10618-012-0300-z

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