Abstract
With the rapid development of information technology, the structure of data resources is becoming more and more complex, and outlier mining is attracting more and more attention. Based on Gaussian kernel function, this paper considers three neighbors: k nearest neighbors, reverse k neighbors and shared nearest neighbors. A local outlier detection algorithm based on Gaussian kernel function is proposed. Firstly, the algorithm stores the nearest neighbors of each data object through kNN maps, including k-nearest neighbors, reverse k-nearest neighbors, and shared nearest neighbors, forming a kernel neighbor set S. Secondly, Estimating density of data objects through kernel density estimation KDE method. Finally, the relative density outlier factor RDOF is used to estimate the degree of data objects deviating from the neighborhood, and then determines whether the data objects are outliers, and the validity of the algorithm is proved on the real and synthetic data sets.
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Zhang, Z., Liu, J., Miao, C. (2019). Local Outlier Detection Algorithm Based on Gaussian Kernel Density Function. In: Peng, H., Deng, C., Wu, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2018. Communications in Computer and Information Science, vol 986. Springer, Singapore. https://doi.org/10.1007/978-981-13-6473-0_29
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DOI: https://doi.org/10.1007/978-981-13-6473-0_29
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