S-Space College of Engineering/Engineering Practice School (공과대학/대학원) Dept. of Industrial Engineering (산업공학과) Journal Papers (저널논문_산업공학과)
Fast outlier detection for very large log data
- Kim, Seung; Cho, Nam Wook; Kang, Bokyoung; Kang, Suk Ho
- Issue Date
- PERGAMON-ELSEVIER SCIENCE LTD
- EXPERT SYSTEMS WITH APPLICATIONS; Vol.38 8; 9587-9596
- Density-based outlier detection; Kd-tree; Approximated k-nearest neighbors; Intrusion (novelty, anomaly) detection
- 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.
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