Abstract
The LOF data anomaly detection method has some defects, such as the value of k has great influence on the accuracy of detection results, and the selection of k value usually adopts trial method, which consumes a lot of calculation time. Therefore, this paper proposes an anomaly detection method for LOF data based on sample parameter selection, Tagged according to the sample data set point of normal and abnormal point, the adaptive selection of k value and outlier detection, so as to improve the accuracy of data outlier detection and calculation speed, and through the example of meteorological data outliers detection showed that LOF abnormal data points based on sample parameter selection method in the detection accuracy and reliability are improved significantly.
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