A comprehensive survey of numeric and symbolic outlier mining techniques

Authors:
Malik Agyemang;Ken Barker;Rada Alhajj
Affiliations:
Department of Computer Science, University of Calgary, 2500 University Drive N.W. Calgary, AB, Canada T2N 1N4. E-mail: {agyemang,barker,alhajj}@cpsc.ucalgary.ca;Department of Computer Science, University of Calgary, 2500 University Drive N.W. Calgary, AB, Canada T2N 1N4. E-mail: {agyemang,barker,alhajj}@cpsc.ucalgary.ca;Department of Computer Science, University of Calgary, 2500 University Drive N.W. Calgary, AB, Canada T2N 1N4. E-mail: {agyemang,barker,alhajj}@cpsc.ucalgary.ca
Venue:
Intelligent Data Analysis
Year:
2006

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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.