A k-Nearest Neighbor Medoid-Based Outlier Detection Algorithm
Xiaochun Wang, Hongchao Jiang, Baoqi Yang · 2021
Outlier detection techniques are well known for identifying a small amount of data objects named outliers that are far away from clusters and exist in sparse regions of data space. However, most outlier detection algorithms based on k nearest neighbors are sensitive to parameter k. The outlier detection algorithms based on clustering rely on specific clustering algorithms, and outliers are by-products. To partially circumvent these problems, motivated by the medoid concept of K-medoids clustering algorithms, in this paper, we propose a k-nearest neighbor medoid-based outlier detection method that is easy to implement and can provide competing performances with existing solutions. At the same time, a method to determine the parameter k is proposed in combination with the outlier detection algorithm proposed in this paper. Experiments performed on real datasets demonstrate the efficacy of our method.