Support Vector Clustering for Outlier Detection

Hai Lei Wang; Wen Bo Li; Bing Yu  Sun

doi:10.4028/www.scientific.net/AMR.756-759.493

Paper Titles

Simulation and Analysis of PMSM Based on Space Vector Control Strategy
p.472

Comparison of the Application of 3G and COFDM in UHV Transmission Lines Inspection
p.477

Research of Node Energy Consumption Based on Dynamic Power Management in Wireless Greenhouse Control System
p.482

Design and Implementation on Auto Test Set of an Airborne Radar
p.489

Support Vector Clustering for Outlier Detection
p.493

Complete Coverage Path Planning and Obstacle Avoidance Strategy of the Robot
p.497

An Evacuation Decision Making Model for Firefighters in Ad Hoc Robot Network
p.504

Vision Control System of Pipe Welding Robot
p.509

Based on Neural Network PID Controller Design and Simulation
p.514

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 756-759Support Vector Clustering for Outlier Detection

Support Vector Clustering for Outlier Detection

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.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 756-759)

Pages:

493-496

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.756-759.493

Citation:

Cite this paper

Online since:

September 2013

Authors:

Hai Lei Wang, Wen Bo Li, Bing Yu Sun

Keywords:

Nearest Distance, Outlier Detection, Support Vector Clustering

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] David, Clustering via Hilbert Space, Physica A, 302(2001), pp.70-79.

Google Scholar

[2] M.J. Tax Dvaid, Support Vector Data Description, Machine Learning , 54, 45-66, (2004).

Google Scholar

[3] B. Scholkopf ,A.J. Smolad, New Support Vector Algorithmsm, Technical Report , GMD first and Australian National University, (1998).

Google Scholar

[4] E. Knorr, V. Tuckkov, Distance-based outliers: algorithm and applications, VLDB journal, 8(3): pp.237-253, (2000).

Google Scholar

[5] B. Sch¨olkopf_ , R. Williamson_ , Alex Smola_ , John Shawe-Taylor , J. Platt Support Vector Method for Novelty Detection, S.A. Solla, T.K. Leen and K. -R. M¨ uller (eds. ), 582–588MIT Press, (2000).

Google Scholar

[6] J. Han, M. Kamber. Data Mining : Concepts and Techniques. Morgan Kaufmann Publishers. Inc, (2001).

Google Scholar

[7] C. Blake, UCI repository of machine learning databases, Department of Information and Computer Science, University of California, Irvine, CA, http: /www. ics. uci. edu/~mlearn/MLRepository. html.

Google Scholar