Local Outlier Detection Algorithm Based on Coefficient of Variation

Shi Bo Zhou; Wei Xiang Xu

doi:10.4028/www.scientific.net/AMM.635-637.1723

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 635-637Local Outlier Detection Algorithm Based on...

Local Outlier Detection Algorithm Based on Coefficient of Variation

Abstract:

Local outliers detection is an important issue in data mining. By analyzing the limitations of the existing outlier detection algorthms, a local outlier detection algorthm based on coefficient of variation is introduced. This algorthms applies K-means which is strong in outliers searching, divides data set into sections, puts outliers and their nearing clusters into a local neighbourhood, then figures out the local deviation factor of each local neighbourhood by coefficient of variation, as a result, local outliers can more likely be found.The heoretic analysis and experimental results indicate that the method is ef fective and efficient.

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Periodical:

Applied Mechanics and Materials (Volumes 635-637)

Pages:

1723-1728

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.635-637.1723

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Online since:

September 2014

Authors:

Shi Bo Zhou*, Wei Xiang Xu

Keywords:

Clustering, Local Deviation Factor, Local Outlier Detection, Variation Coefficient

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* - Corresponding Author

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