Outlier Detection for Data Using Density-Based Technique

Handling anomalies in high-dimensional information viably and effectively is as yet a difficult issue in AI. Distinguishing anomalies has an expansive scope of true applications. High-dimensional information may trigger the separation fixation issue, though the exception discovery requires fitting qualities for parameters, making models high unpredictable and progressively touchy. To defeat these issues right now idea called nearby projection score (LPS) is acquainted with speak to deviation level of perception to its neighbors.