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Local Outlier Detection with Interpretation

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  • Xuan-Hong Dang, Denmark
  • Barbora Micenková, Denmark
  • Ira Assent
  • Raymond T. Ng, University of British Columbia, Canada
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.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III
EditorsHendrik Blockeel, Kristian Kersting , Siegfried Nijssen, Filip Železný
Number of pages17
PublisherSpringer VS
Publication year2013
ISBN (print)978-3-642-40993-6
ISBN (Electronic)978-3-642-40994-3
Publication statusPublished - 2013
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2013 - Prague, Czech Republic
Duration: 23 Sep 201327 Sep 2013


ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2013
LandCzech Republic
SeriesLecture Notes in Computer Science

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