Affiliations: INRIA Sophia-Antipolis, Sophia-Antipolis, France | [x] LIAAD, University of Porto, Porto, Portugal | [y] Fraunhofer IAIS, Sankt Augustin, Germany
Correspondence:
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Corresponding author: Florent Masseglia, INRIA Sophia-Antipolis, 2004 route des lucioles – BP 93, 06902 Sophia-Antipolis, France. E-mail: florent.Masseglia@sophia.inria.fr.
Abstract: Outlyingness is a subjective concept relying on the isolation level of a (set of) record(s). Clustering-based outlier detection is a field that aims to cluster data and to detect outliers depending on their characteristics (i.e. small, tight and/or dense clusters might be considered as outliers). Existing methods require a parameter standing for the “level of outlyingness”, such as the maximum size or a percentage of small clusters, in order to build the set of outliers. Unfortunately, manually setting this parameter in a streaming environment should not be possible, given the fast time response usually needed. In this paper we propose Wod, a method that separates outliers from clusters thanks to a natural and effective principle. The main advantages of Wod are its ability to automatically adjust to any clustering result and to be parameterless.
