Support Vector Clustering for Outlier Detection

DOI

10.2991/iccia.2012.83How to use a DOI?

Keywords

Support vector clustering, Outlier detection, Nearest Distance.

Abstract

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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - 10.2991/iccia.2012.83
ID  - Wang2014/05
ER  -