Abstract
With the advent of the era of big data, while machines process data instead of humans, timely detection of abnormal conditions in data through algorithms has become the focus of research, and the importance of data detection accuracy has also increased. Therefore, based on the traditional kernel density estimation theory, this paper adds adaptive diffusion equation theory to improve it, selects the best window width value to achieve a high degree of data distribution fitting, and further formulates detection rules. Finally, compared with other traditional detection methods, the results show that the method proposed in this paper can improve the accuracy and stability of abnormal data detection.
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