GO-PEAS: a scalable yet accurate grid-based outlier detection method using novel pruning searching techniques

Li, Hongzhou and Zhang, Ji and Luo, Yonglong and Chen, Fulong and Chang, Liang (2016) GO-PEAS: a scalable yet accurate grid-based outlier detection method using novel pruning searching techniques. In: 2nd Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2016), 2-5 Feb 2016, 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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © Springer International Publishing Switzerland 2016.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 20 Feb 2017 06:33
Last Modified: 30 Oct 2017 05:07
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Identification Number or DOI: 10.1007/978-3-319-28270-1 11
URI: http://eprints.usq.edu.au/id/eprint/30402

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