 Anomaly Detection Algorithm Based on Subspace Local Density EstimationChunkai Zhang and
Ao Yin
International Journal of Web Services Research (IJWSR), 2019, vol. 16, issue 3, 44-58 Abstract: In this article, the authors propose a novel anomaly detection algorithm based on subspace local density estimation. The key insight of the proposed algorithm is to build multiple trident trees, which can implement the process of building subspace and local density estimation. Each trident tree (T-tree) is constructed recursively by splitting the data outside of 3 sigma into the left or right subtree and splitting the remaining data into the middle subtree. Each node in trident tree records the number of instances that falls on this node, so each trident tree can be used as a local density estimator. The density of each instance is the average of all trident tree evaluation instance densities, and it can be used as the anomaly score of instances. Since each trident tree is constructed according to 3 sigma principle, it can obtain good anomaly detection results without a large tree height. Theoretical analysis and experimental results show that the proposed algorithm is effective and efficient. Date: 2019 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:igg:jwsr00:v:16:y:2019:i:3:p:44-58 Access Statistics for this article International Journal of Web Services Research (IJWSR) is currently edited by Liang-Jie Zhang More articles in International Journal of Web Services Research (IJWSR) from IGI Global |
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