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Article type: Research Article
Authors: Agyemang, Malik | Barker, Ken | Alhajj, Rada
Affiliations: Department of Computer Science, University of Calgary, 2500 University Drive N.W. Calgary, AB, Canada T2N 1N4. E-mail: agyemang@cpsc.ucalgary.ca, barker@cpsc.ucalgary.ca, alhajj@cpsc.ucalgary.ca
Abstract: Data that appear to have different characteristics than the rest of the population are called outliers. Identifying outliers from huge data repositories is a very complex task called outlier mining. Outlier mining has been akin to finding needles in a haystack. However, outlier mining has a number of practical applications in areas such as fraud detection, network intrusion detection, and identification of competitor and emerging business trends in e-commerce. This survey discuses practical applications of outlier mining, and provides a taxonomy for categorizing related mining techniques. A comprehensive review of these techniques with their advantages and disadvantages along with some current research issues are provided.
Keywords: Symbolic, rule-based, distance-based, depth-based, distribution-based, outliers, interestingness, unexpectedness, taxonomy, web-based, exception patterns
DOI: 10.3233/IDA-2006-10604
Journal: Intelligent Data Analysis, vol. 10, no. 6, pp. 521-538, 2006
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