Atypicity Detection in Data Streams: a Self-Adjusting Approach

Alice Marascu (1) , Florent Masseglia (1)

1 AxIS - Usage-centered design, analysis and improvement of information systems

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.

Domaines: Apprentissage [cs.LG]; Base de données [cs.DB]; Intelligence artificielle [cs.AI]

Alice Marascu, Florent Masseglia. Atypicity Detection in Data Streams: a Self-Adjusting Approach. Intelligent Data Analysis, 2011, 15 (1), pp.89-105. ⟨10.3233/IDA-2010-0457⟩. ⟨hal-00789034⟩