基于近邻传播的离群点检测算法.

Outliers are a class of objects with different properties from other normal points, whose detection technology in various industries has a wide application to maintain the purity of data and ensure the safety of the industry. Most of the existing algorithms a re based on distance, density, and other traditional methods to detect outliers. This paper assigned each object an "isolation degree", the degree of isolation of the point relative to adjacent points, which could identify outliers by sorting, that was more efficient. It proposed the detection technology APO by improving and optimizing the AP clustering algorithm. It introduced the outlie r module and processed the isolated information of points. In addition, it added the amplification factor to make the difference between the outliers and the normal points more obvious. By increasing the sensitivity of the algorithm to outliers, it improved the accuracy of the algorithm. The experiment used simulated dataset real datasets, who ' s the results showed that the algorithm was more sensitive and it detected outliers more accurately than AP algorithm. In addition, this algorithm can cluster outliers while detecting outliers, which is not available in other detection algorithms. [ABSTRACT FROM AUTHOR]