Parameterless Outlier Detection in Data Streams - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2009

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.
No file

Dates and versions

inria-00461827 , version 1 (05-03-2010)


  • HAL Id : inria-00461827 , version 1


Alice Marascu, Florent Masseglia. Parameterless Outlier Detection in Data Streams. ACM symposium on Applied Computing, Mar 2009, Honolulu, United States. pp.1491-1495. ⟨inria-00461827⟩
57 View
0 Download


Gmail Mastodon Facebook X LinkedIn More