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Proceedings Paper

Outlier detection based on variance of angle in high dimensional data
Author(s): Wenting Liu
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Paper Abstract

Outlier detection in high dimensional data is one of the hot areas of data mining. The existing outlier detection methods are based on the distance in Euclidean space. In high-dimensional data, these methods are bound to deteriorate due to the notorious "dimension disaster" which leads to distance measure cannot express the original physical meaning and the low computational efficiency. This paper improves the method of angle-based outlier factor and proposes the method of variance of angle-based outlier factor outlier in mining high dimensional. It introduces the related theories to guarantee the reliability of the method. The empirical experiments on synthetic data sets show the method is efficiency and scalable to high-dimensional data sets.

Paper Details

Date Published: 3 December 2015
PDF: 5 pages
Proc. SPIE 9794, Sixth International Conference on Electronics and Information Engineering, 979407 (3 December 2015); doi: 10.1117/12.2205047
Show Author Affiliations
Wenting Liu, Hohai Univ. (China)

Published in SPIE Proceedings Vol. 9794:
Sixth International Conference on Electronics and Information Engineering
Qiang Zhang, Editor(s)

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