GO-PEAS: a scalable yet accurate grid-based outlier detection method using novel pruning searching techniques
Paper
Paper/Presentation Title | GO-PEAS: a scalable yet accurate grid-based outlier detection method using novel pruning searching techniques |
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Presentation Type | Paper |
Authors | Li, Hongzhou (Author), Zhang, Ji (Author), Luo, Yonglong (Author), Chen, Fulong (Author) and Chang, Liang (Author) |
Editors | Ray, T., Sarker, R. and Li, X. |
Journal or Proceedings Title | Lecture Notes in Computer Science (Book series) |
Journal Citation | 9592, pp. 125-133 |
Number of Pages | 9 |
Year | 2016 |
Publisher | Springer |
Place of Publication | Switzerland |
ISSN | 1611-3349 |
0302-9743 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-319-28270-1_11 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-319-28270-1_11 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-3-319-28270-1 |
Conference/Event | 2nd Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2016) |
Event Details | 2nd Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2016) Parent Australasian Conference on Artificial Life and Computational Intelligence (ACALCI ) Event Date 02 to end of 05 Feb 2016 Event Location Canberra, Australia |
Abstract | In this paper, we propose a scalable yet accurate grid-based outlier detection method called GO-PEAS (stands for Grid-based Outlier detection with Pruning Searching techniques). Innovative techniques are incorporated into GO-PEAS to greatly improve its speed performance, making it more scalable for large data sources. These techniques offer efficient pruning of unnecessary data space to substantially enhance the detection speed performance of GO-PEAS. Furthermore, the detection accuracy of GO-PEAS is guaranteed to be consistent with its baseline version that does not use the enhancement techniques. Experimental evaluation results have demonstrated the improved scalability and good effectiveness of GO-PEAS. |
Keywords | Detection accuracy; Detection speed; Experimental evaluation; Innovative techniques; Large data; Outlier Detection; Searching techniques; Speed performance |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Guilin University of Electronic Technology, China |
Faculty of Health, Engineering and Sciences | |
Anhui Normal University, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q3w59/go-peas-a-scalable-yet-accurate-grid-based-outlier-detection-method-using-novel-pruning-searching-techniques
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