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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Acknowledgement
The authors would like to thank the support from National Science Foundation of China through the research projects (No. 61370050, No. 61572036 and No. 61363030) and Guangxi Key Laboratory of Trusted Software (No. kx201527).
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Li, H., Zhang, J., Luo, Y., Chen, F., Chang, L. (2016). GO-PEAS: A Scalable Yet Accurate Grid-Based Outlier Detection Method Using Novel Pruning Searching Techniques. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_11
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DOI: https://doi.org/10.1007/978-3-319-28270-1_11
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