OutRules: A Framework for Outlier Descriptions in Multiple Context Spaces

  • Emmanuel Müller
  • Fabian Keller
  • Sebastian Blanc
  • Klemens Böhm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7524)

Abstract

Analyzing exceptional objects is an important mining task. It includes the identification of outliers but also the description of outlier properties in contrast to regular objects. However, existing detection approaches miss to provide important descriptions that allow human understanding of outlier reasons. In this work we present OutRules, a framework for outlier descriptions that enable an easy understanding of multiple outlier reasons in different contexts. We introduce outlier rules as a novel outlier description model. A rule illustrates the deviation of an outlier in contrast to its context that is considered to be normal. Our framework highlights the practical use of outlier rules and provides the basis for future development of outlier description models.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aggarwal, C.C., Yu, P.S.: Outlier detection for high dimensional data. In: SIGMOD, pp. 37–46 (2001)Google Scholar
  2. 2.
    Angiulli, F., Fassetti, F., Palopoli, L.: Detecting outlying properties of exceptional objects. ACM Trans. Database Syst. 34(1), 1–62 (2009)CrossRefGoogle Scholar
  3. 3.
    Breunig, M., Kriegel, H.-P., Ng, R., Sander, J.: LOF: Identifying density-based local outliers. In: SIGMOD, pp. 93–104 (2000)Google Scholar
  4. 4.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Comput. Surv. 41(3) (2009)Google Scholar
  5. 5.
    Keller, F., Müller, E., Böhm, K.: HiCS: High contrast subspaces for density-based outlier ranking. In: ICDE (2012)Google Scholar
  6. 6.
    Knorr, E.M., Ng, R.T.: Finding intensional knowledge of distance-based outliers. In: VLDB, pp. 211–222 (1999)Google Scholar
  7. 7.
    Kriegel, H.-P., Kröger, P., Schubert, E., Zimek, A.: Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 831–838. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Müller, E., Schiffer, M., Gerwert, P., Hannen, M., Jansen, T., Seidl, T.: SOREX: Subspace Outlier Ranking Exploration Toolkit. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010, Part III. LNCS, vol. 6323, pp. 607–610. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Müller, E., Schiffer, M., Seidl, T.: Statistical selection of relevant subspace projections for outlier ranking. In: ICDE, pp. 434–445 (2011)Google Scholar
  10. 10.
    Smets, K., Vreeken, J.: The odd one out: Identifying and characterising anomalies. In: SDM, pp. 804–815 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Emmanuel Müller
    • 1
  • Fabian Keller
    • 1
  • Sebastian Blanc
    • 1
  • Klemens Böhm
    • 1
  1. 1.Karlsruhe Institute of Technology (KIT)Germany

Personalised recommendations