151. Data Intensive vs Sliding Window Outlier Detection in the Stream Data — An Experimental Approach
- Author
-
Łukasz Wróbel, Mateusz Kalisch, Marcin Michalak, Piotr Przystałka, and Marek Sikora
- Subjects
Sequence ,Local outlier factor ,Basis (linear algebra) ,Computer science ,business.industry ,Pattern recognition ,02 engineering and technology ,Object (computer science) ,computer.software_genre ,020204 information systems ,Sliding window protocol ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Anomaly detection ,Point (geometry) ,Data mining ,Artificial intelligence ,business ,Stream data ,computer - Abstract
In the paper a problem of outlier detection in the stream data is raised. The authors propose a new approach, using well known outlier detection algorithms, of outlier detection in the stream data. The method is based on the definition of a sliding window, which means a sequence of stream data observations from the past that are closest to the newly coming object. As it may be expected the outlier detection accuracy level of this model becomes worse than the accuracy of the model that uses all historical data, but from the statistical point of view the difference is not significant. In the paper several well known methods of outlier detection are used as the basis of the model.
- Published
- 2016
- Full Text
- View/download PDF