Angle-based high dimensional data outlier detection method

Abstract

The invention discloses an angle-based high dimensional data outlier detection method and belongs to the technical field of outlier data mining. The method comprises the specific steps that 1, k nearest neighbour points of each data point A belonging to a data set D are obtained in the data set D; 2, an angle-based outlier factor of each data point is calculated; 3, the outlier factors of the data points are ranked, and a point set with the minimum outlier factor is selected as an outlier point set with the largest data outlier degree; 4, outlier data are determined. According to the method, outlier data concealed in large-scale high dimensional data can be found efficiently and rapidly, the problem of curse of dimensionality of the outlier detection method based on high dimensional distance, nearest neighbour and the like can be effectively solved, and the method can be widely applied in high dimensional data for credit card fraud detection, traffic accident detection, scientific data measurement abnormal detection and the like.

Description

technical field [0001] The invention relates to an angle-based high-dimensional data outlier detection method, which belongs to the technical field of outlier data mining. Background technique [0002] Outlier data mining technology is one of the research hotspots in the field of data mining at present, and it is widely used in network traffic intrusion detection, traffic accident detection, scientific data measurement anomaly detection and other fields. At present, the existing outlier data mining is mainly based on the concept of distance or nearest neighbor for outlier mining. In high-dimensional data, high-dimensional space distance and nearest neighbor no longer have the characteristics of Euclidean space, and there will be a disaster of distance dimension. . In high-dimensional data, because the outliers are far away from other data points, the angle between the vectors formed by the outliers and other points does not change much, while the non-outliers are surrounded...

AI Technical Summary

Problems solved by technology

At present, the existing outlier data mining is mainly based on the concept of distance or nearest neighbor for outlier mining. In high-dimensional data, high-dimensional space distance and nearest neighbor no longer have the characteristics of Euclidean space, and there will be a disaster of distance dimension.

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