Reduction in Execution Cost of k-Nearest Neighbor Based Outlier Detection Method

Abstract

Outlier detection is an important task as it leads to the discovery of critical information in a variety of the application domains. The variants of k-nearest neighbor based outlier detection method have been successfully applied over decades. However, these approaches have high execution time as they compute a score (known as outlier score) for each data point. In this paper, we propose a method to reduce the execution time of k-nearest neighbor based algorithms. Proposed method quickly identifies the data points which are normal and therefore outlier score for such points need not be computed in further processing. The proposed method is generic and can be applied to improve the execution efficiency of many density-based and distance-based outlier detection methods. Proposed work is compared with other existing methods and the result shows that the proposed work outperforms other methods.