An angle and density-based method for key points detection

This paper presents an angle and density-based data preprocessing method. It can be used to simultaneously identify outliers, boundary points and center points of clusters. Boundary points and outliers are generally located around the margin of densely distributed data such as a cluster. Detecting boundary points and outliers is often more interesting than detecting normal observations since they represent valid, interesting, and potentially valuable patterns. We propose an approach based on the idea that boundary points are characterized by a lower local density and by a smaller angle variance than that of their neighbors. Outliers, boundary points and inner points can be identified by both angle and density measurements. Experimental results obtained for several test cases demonstrate the effectiveness and efficiency of our method.