Name
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 (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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1529282.1529615 | SAC |
Keywords | Field | DocType |
effective principle,cluster data,parameter standing,main advantage,isolation level,clustering-based outlier detection,clustering result,dense cluster,parameterless outlier detection,fast time response,data stream,small cluster,outliers,data streams,outlier detection | Anomaly detection,Data mining,Cluster (physics),Isolation (database systems),Data stream mining,Pattern recognition,Computer science,Outlier,Artificial intelligence,Cluster analysis,Time response | Conference |
Citations | PageRank | References |
2 | 0.37 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alice Marascu | 1 | 70 | 7.94 |
| Florent Masseglia | 2 | 408 | 43.08 |