Automatic detection of boundary points based on local geometrical measures

Abstract

This paper presents an angle and density-based data preprocessing method. It can be used to simultaneously identify outliers and boundary points (called uniformly boundary points). Detecting boundary points is often more interesting than detecting normal points, since they represent valid, interesting, and potentially valuable patterns. An efficient local geometry-based method is proposed for detecting such points by both angle and density measures. The unified measure is adaptive and stable by combining multiple features (angles and density), which can be used to evaluate to what degree a given point is a boundary point. Compared with two related state-of-the-art approaches, our method better reflects the characteristics of the data and provides similar but accuracies for more data set. Experimental results obtained for a number of synthetic and real-world data sets demonstrate the effectiveness and efficiency of our method.