Support Vector Clustering for Outlier Detection
Hai-Lei Wang, Wen-Bo Li, Bing-Yu Sun
Available Online May 2014.
- https://doi.org/10.2991/iccia.2012.83How to use a DOI?
- Support vector clustering, Outlier detection, Nearest Distance.
- In this paper a novel Support vector clustering(SVC) method for outlier detection is proposed. Outlier detection algorithms have application in several tasks such as data mining, data preprocessing, data filter-cleaner, time series analysis and so on. Traditionally outlier detection methods are mostly based on modeling data based on its statistical properties and these approaches are only preferred when large scale set is available. To solve this problem, in this paper we focus on establishing the context of support vector clustering approach for outlier detection. Compared to traditional outlier detection methods , the performance of the SVC is not sensitive to the selection of needed parameters. The experiment results proved the efficiency of our method.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Hai-Lei Wang AU - Wen-Bo Li AU - Bing-Yu Sun PY - 2014/05 DA - 2014/05 TI - Support Vector Clustering for Outlier Detection BT - Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012) PB - Atlantis Press SP - 343 EP - 345 SN - 1951-6851 UR - https://doi.org/10.2991/iccia.2012.83 DO - https://doi.org/10.2991/iccia.2012.83 ID - Wang2014/05 ER -