Parameterless outlier detection in data streams

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 (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. Categories and Subject Descriptors H.4 [Information Systems Applications]: Miscellaneous Keywords Data Streams, Outliers, Parameterless

• Type: Conference
• Year: 2009
• Where: SAC
• Authors: Alice Marascu, Florent Masseglia
• Keywords: And/or Dense Clusters, Applied Computing, Clustering-based Outlier Detection, Outliers, SAC 2009