A comprehensive survey of anomaly detection techniques for high dimensional big data Thudumu, Srikanth, Branch, P, Jin, J and Singh, J 2020, A comprehensive survey of anomaly detection techniques for high dimensional big data, Journal of Big Data, vol. 7, pp. 1-30, doi: 10.1186/s40537-020-00320-x. Attached Files Name Description MIMEType Size Downloads Title A comprehensive survey of anomaly detection techniques for high dimensional big data Author(s) Thudumu, Srikanth Branch, P Jin, J Singh, J Journal name Journal of Big Data Volume number 7 Article ID 42 Start page 1 End page 30 Total pages 30 Publisher SpringerOpen Place of publication London, Eng. Publication date 2020-07-02 ISSN 2196-1115 Keyword(s) ALGORITHM Anomaly detection Big data Big dimensionality Big dimensionality tools Computer Science Computer Science, Theory & Methods ESTIMATOR GEOMETRIC REPRESENTATION High dimensionality INDEPENDENCE INTRINSIC DIMENSIONALITY MINING OUTLIERS NOVELTY DETECTION PARALLEL OUTLIER DETECTION Science & Technology STRATEGIES Technology The curse of big dimensionality The curse of dimensionality Language eng DOI 10.1186/s40537-020-00320-x Field of Research 08 Information and Computing Sciences HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30158643 Document type: Journal Article Collections: Open Access Collection A2I2 (Applied Artificial Intelligence Institute) Related Links Link Description Link to full-text (open access)   Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Thu, 18 Nov 2021, 07:53:42 EST Thu, 18 Nov 2021, 16:03:01 EST Fri, 19 Nov 2021, 07:14:15 EST Sat, 04 Dec 2021, 01:31:16 EST Sat, 04 Dec 2021, 01:48:00 EST Mon, 27 Jun 2022, 11:32:51 EST Mon, 27 Jun 2022, 12:45:52 EST Mon, 27 Jun 2022, 12:47:46 EST Tue, 05 Jul 2022, 16:14:27 EST Tue, 19 Jul 2022, 10:51:31 EST Fri, 29 Jul 2022, 02:35:05 EST Filtered Full Citation counts:   Cited 46 times in TR Web of Science Cited 62 times in Scopus Search Google Scholar Access Statistics: 9 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Thu, 18 Nov 2021, 07:53:42 EST