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 (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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.3233/IDA-2010-0457 | Intell. Data Anal. |
Keywords | Field | DocType |
effective principle,cluster data,parameter standing,main advantage,isolation level,atypicity detection,clustering-based outlier detection,clustering result,dense cluster,fast time response,data stream,small cluster,self-adjusting approach | Data mining,Cluster (physics),Anomaly detection,Data stream mining,Isolation (database systems),Pattern recognition,Computer science,Outlier,Artificial intelligence,Cluster analysis,Machine learning,Time response | Journal |
Volume | Issue | ISSN |
15 | 1 | 1088-467X |
Citations | PageRank | References |
1 | 0.35 | 20 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alice Marascu | 1 | 70 | 7.94 |
| Florent Masseglia | 2 | 408 | 43.08 |