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Anomaly Detection Algorithm Based on Subspace Local Density Estimation
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-t...
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Published in: | International journal of web services research 2019-07, Vol.16 (3), p.44-58 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | 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. |
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ISSN: | 1545-7362 1546-5004 |
DOI: | 10.4018/IJWSR.2019070103 |