HIGHLIGHTS
SUMMARY
Due to the diversity and complexity of data, there are inevitable data points in a set of data points that are different from the majority of data. Building an OD method for large-scale high-dimensional data with good performance for open environments will be very important for open applications with high-dimensional, multidistribution characteristics and massive data. Traditional model-based methods fit data objects to the model to determine whether the data is an outlier, but this method is not suitable for multisource heterogeneous data. Distance-based outlier detection is effective in solving . . .
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