Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)

Support Vector Clustering for Outlier Detection

Authors
Hai-Lei Wang, Wen-Bo Li, Bing-Yu Sun
Corresponding Author
Hai-Lei Wang
Available Online May 2014.
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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Volume Title
Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)
Series
Advances in Intelligent Systems Research
Publication Date
May 2014
ISBN
978-94-91216-41-1
ISSN
1951-6851
DOI
10.2991/iccia.2012.83How to use a DOI?
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/).

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