Outlier detection based on variance of angle in high dimensional data

Wenting Liu

doi:10.1117/12.2205047

3 December 2015 Outlier detection based on variance of angle in high dimensional data

Wenting Liu

Proceedings Volume 9794, Sixth International Conference on Electronics and Information Engineering; 979407 (2015) https://doi.org/10.1117/12.2205047
Event: Sixth International Conference on Electronics and Information Engineering, 2015, Dalian, China

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.

Citation Download Citation

Wenting Liu "Outlier detection based on variance of angle in high dimensional data", Proc. SPIE 9794, Sixth International Conference on Electronics and Information Engineering, 979407 (3 December 2015); https://doi.org/10.1117/12.2205047

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