Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/40918
Title: Correlation-based detection of attribute outliers
Authors: Koh, J.L.Y.
Lee, M.L. 
Hsu, W. 
Lam, K.T.
Keywords: Data cleaning
Outlier detection
Issue Date: 2007
Source: Koh, J.L.Y.,Lee, M.L.,Hsu, W.,Lam, K.T. (2007). Correlation-based detection of attribute outliers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4443 LNCS : 164-175. ScholarBank@NUS Repository.
Abstract: An outlier is an object that does not conform to the normal behavior of the data set. In data cleaning, outliers are identified for data noise reduction. In applications such as fraud detection, and stock market analysis, outliers suggest abnormal behavior requiring further investigation. Existing outlier detection methods have focused on class outliers and research on attribute outliers is limited, despite the equal role attribute outliers play in depreciating data quality and reducing data mining accuracy. In this paper, we propose a novel method to detect attribute outliers from the deviating correlation behavior of attributes. We formulate three metrics to evaluate outlier-ness of attributes, and introduce an adaptive factor to distinguish outliers from non-outliers. Experiments with both synthetic and real-world data sets indicate that the proposed method is effective in detecting attribute outliers. © Springer-Verlag Berlin Heidelberg 2007.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/40918
ISBN: 9783540717027
ISSN: 03029743
Appears in Collections:Staff Publications

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