LSHiForest: A Generic Framework for Fast Tree Isolation Based Ensemble Anomaly Analysis

Full text for this resource is not available from the Research Repository.

Zhang, X, Dou, W, He, Q, Zhou, Rui ORCID: 0000-0001-6807-4362, Leckie, C, Kotagiri, R and Salcic, Z (2017) LSHiForest: A Generic Framework for Fast Tree Isolation Based Ensemble Anomaly Analysis. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE 2017), 19 April 2017-22 April 2017, San Diego, California, USA.

Dimensions Badge

Altmetric Badge

Item type Conference or Workshop Item (Paper)
URI https://vuir.vu.edu.au/id/eprint/36373
DOI https://doi.org/10.1109/ICDE.2017.145
Official URL https://ieeexplore.ieee.org/document/7930041/
ISBN 9781509065431
Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > Faculty/School/Research Centre/Department > Centre for Applied Informatics
Current > Division/Research > College of Science and Engineering
Keywords outlier detection; fraud detection; Locality-Sensitive Hashing; big data
Citations in Scopus 33 - View on Scopus
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login