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Fast outlier detection for very large log data

Cited 19 time in Web of Science Cited 33 time in Scopus
Authors
Kim, Seung; Cho, Nam Wook; Kang, Bokyoung; Kang, Suk Ho
Issue Date
2011-08-01
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
EXPERT SYSTEMS WITH APPLICATIONS; Vol.38 8; 9587-9596
Keywords
Density-based outlier detectionKd-treeApproximated k-nearest neighborsIntrusion (novelty, anomaly) detection
Abstract
Density-based outlier detection identifies an outlying observation with reference to the density of the surrounding space. In spite of the several advantages of density-based outlier detections, its computational complexity remains one of the major barriers to its application. The purpose of the present study is to reduce the computation time of LOF (Local Outlier Factor), a density-based outlier detection algorithm. The proposed method incorporates kd-tree indexing and an approximated k-nearest neighbors search algorithm (ANN). Theoretical analysis on the approximation of nearest neighbor search was conducted. A set of experiments was conducted to examine the performance of the proposed algorithm. The results show that the method can effectively detect local outliers in a reduced computation time. (C) 2011 Elsevier Ltd. All rights reserved.
ISSN
0957-4174
Language
English
URI
https://hdl.handle.net/10371/74917
DOI
https://doi.org/10.1016/j.eswa.2011.01.162
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Industrial Engineering (산업공학과)Journal Papers (저널논문_산업공학과)
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