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Author Koufakou, A. ♦ Ortiz, E.G. ♦ Georgiopoulos, M. ♦ Anagnostopoulos, G.C. ♦ Reynolds, K.M.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2007
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Intrusion detection ♦ Credit cards ♦ Clustering algorithms ♦ Artificial intelligence ♦ Scattering ♦ Frequency ♦ Cleaning ♦ Diseases ♦ Scalability ♦ Explosions
Abstract Outlier detection has received significant attention in many applications, such as detecting credit card fraud or network intrusions. Most existing research focuses on numerical datasets, and cannot directly apply to categorical sets where there is little sense in calculating distances among data points. Furthermore, a number of outlier detection methods require quadratic time with respect to the dataset size and usually multiple dataset scans. These characteristics are undesirable for large datasets, potentially scattered over multiple distributed sites. In this paper, we introduce Attribute Value Frequency (A VF), a fast and scalable outlier detection strategy for categorical data. A VF scales linearly with the number of data points and attributes, and relies on a single data scan. AVF is compared with a list of representative outlier detection approaches that have not been contrasted against each other. Our proposed solution is experimentally shown to be significantly faster, and as effective in discovering outliers.
Description Author affiliation: Univ. of Central Florida, Orlando (Koufakou, A.; Ortiz, E.G.; Georgiopoulos, M.)
ISBN 9780769530154
ISSN 10823409
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2007-10-29
Publisher Place Greece
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 483.16 kB
Page Count 8
Starting Page 210
Ending Page 217


Source: IEEE Xplore Digital Library