Publications
Detailed Information
Fast outlier detection for very large log data
Cited 24 time in
Web of Science
Cited 38 time in Scopus
- Authors
- Issue Date
- 2011-08-01
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Citation
- EXPERT SYSTEMS WITH APPLICATIONS; Vol.38 8; 9587-9596
- Keywords
- Density-based outlier detection ; Kd-tree ; Approximated k-nearest neighbors ; Intrusion (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
- Files in This Item:
- There are no files associated with this item.
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.