Local Outlier Detection Algorithm Based on Coefficient of Variation

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 d...

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Bibliographic Details
Published inApplied Mechanics and Materials Vol. 635-637; pp. 1723 - 1728
Main Authors Zhou, Shi Bo, Xu, Wei Xiang
Format Journal Article
LanguageEnglish
Published Zurich Trans Tech Publications Ltd 01.09.2014
Subjects
Variation Coefficient
Local Outlier Detection
Local Deviation Factor
Clustering
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Summary: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.
Bibliography:Selected, peer reviewed papers from the 4th International Conference on Advanced Design and Manufacturing Engineering (ADME 2014), July 26-27, 2014, Hangzhou, China
ISBN:3038352578
9783038352570
ISSN:1660-9336
1662-7482
1662-7482
DOI:10.4028/www.scientific.net/AMM.635-637.1723