Local Outlier Detection with Interpretation

  • Xuan Hong Dang
  • Barbora Micenková
  • Ira Assent
  • Raymond T. Ng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8190)


Outlier detection aims at searching for a small set of objects that are inconsistent or considerably deviating from other objects in a dataset. Existing research focuses on outlier identification while omitting the equally important problem of outlier interpretation. This paper presents a novel method named LODI to address both problems at the same time. In LODI, we develop an approach that explores the quadratic entropy to adaptively select a set of neighboring instances, and a learning method to seek an optimal subspace in which an outlier is maximally separated from its neighbors. We show that this learning task can be solved via the matrix eigen-decomposition and its solution contains essential information to reveal features that are most important to interpret the exceptional properties of outliers. We demonstrate the appealing performance of LODI via a number of synthetic and real world datasets and compare its outlier detection rates against state-of-the-art algorithms.


Outlier Detection Subspace Cluster Local Outlier Neighboring Object Quadratic Entropy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xuan Hong Dang
    • 1
  • Barbora Micenková
    • 1
  • Ira Assent
    • 1
  • Raymond T. Ng
    • 2
  1. 1.Aarhus UniversityDenmark
  2. 2.University of British ColumbiaCanada

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