Outlier Detection Algorithm Based on Approximate Outlier Factor

Aiming at the problem that the result of outlier detection algorithm based on clustering is coarser and not very accurate, this paper proposes an outlier detection algorithm based on Approximate Outlier Factor(AOF). This algorithm presents the definition of the similarity distance and outlier similarity coefficient, and provides a pruning strategy based on similarity distance to reduce the suspect candidate sets to decrease the computational complexity. Experiments are carried out with public datasets Iris, Labor and Segment-test, and results show that the performance of detecting outlier and reducing candidate set of this algorithm is effective compared with the classical outlier detection algorithm.