Abstract
The outlier detection algorithm based on reverse k-nearest neighbors can detect isolated points. The time complexity of finding the k-nearest neighbor is O(kN 2), which is not suitable for large data set, and the selection of the parameters k have a great impact on getting the outliers in large data set. This paper used an adaptive method to determine the parameters k, and proposed an efficient pruning method by the triangle inequality, which reduced the computation in detecting outliers. The theoretical analysis and experimental results demonstrated the feasibility and efficiency of the algorithm.
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Acknowledgments
This work is supported partly by National Nature Science Foundation of China (60873247), Science and Technology Plan in Colleges and Universities of Shandong Province (J12LN21).
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Fangfang, X., Liancheng, X., Xuezhi, C., Zhenfang, Z. (2014). An Improved Outlier Detection Algorithm Based on Reverse K-Nearest Neighbors of Adaptive Parameters. In: Li, S., Jin, Q., Jiang, X., Park, J. (eds) Frontier and Future Development of Information Technology in Medicine and Education. Lecture Notes in Electrical Engineering, vol 269. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7618-0_47
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DOI: https://doi.org/10.1007/978-94-007-7618-0_47
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