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Atypicity detection in data streams: A self-adjusting approach

Atypicity detection in data streams: A self-adjusting approach 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Intelligent Data Analysis IOS Press

Atypicity detection in data streams: A self-adjusting approach

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Publisher
IOS Press
Copyright
Copyright © 2011 by IOS Press, Inc
ISSN
1088-467X
eISSN
1571-4128
DOI
10.3233/IDA-2010-0457
Publisher site
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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.

Journal

Intelligent Data Analysis – IOS Press

Published: Jan 1, 2011

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