An angle and density-based method for key points detection

机译：基于角度和密度的关键点检测方法

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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.

机译：本文提出了一种基于角度和密度的数据预处理方法。它可用于同时识别聚类的离群值，边界点和中心点。边界点和离群值通常位于密集分布的数据（如群集）的边缘附近。检测边界点和离群值通常比检测正常观察值更有趣，因为它们代表有效，有趣和潜在有价值的模式。我们提出了一种基于这样的思想的方法，即边界点的特征是与相邻点相比，其局部密度较低且角度变化较小。异常值，边界点和内部点可以通过角度和密度测量来识别。通过几个测试案例获得的实验结果证明了我们方法的有效性和效率。

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《International Joint Conference on Neural Networks》|2016年|3682-3688|共7页
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Xiaojie Li; Jiancheng Lv; Lili Li; Feng Ao;
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Distributed databases; Computer science; Electronic mail; Density measurement; Liver; Fires; Neural networks;

机译：分布式数据库;计算机科学;电子邮件;密度测量;肝脏;火灾;神经网络;

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An angle and density-based method for key points detection

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