BPF: An Effective Cluster Boundary Points Detection Technique
BPF: An Effective Cluster Boundary Points Detection Technique
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BPF：一种有效的聚类边界点检测技术
DOI：
10.1007/978-3-031-12423-5_31
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Vijdan Khalique and Hiroyuki Kitagawa
中科院分区：
文献类型：
--
作者：
Masaya Yamada;Hiroyuki Kitagawa;Toshiyuki Amagasa;Akiyoshi Maton;Vijdan Khalique and Hiroyuki Kitagawa
在数据集中，边界点位于聚类的极端。检测这样的边界点可以提供关于过程的有用信息，并且它可以具有许多现实世界的应用。现有的方法是敏感的离群值，不同密度的集群，需要调整一个以上的参数。在局部离群因子（LOF）算法的基础上，提出了一种边界点检测方法--边界点因子（BPF），BPF通过合并数据集中所有点的LOF值来计算重力值和BPF值。通过使用所有点的BPF分数可以有效地检测边界点，其中边界点往往具有比其他点更大的BPF分数。BPF需要调整一个参数，并且可以与LOF一起使用以分别输出离群值和边界点。在合成数据集和真实的数据集上的实验结果表明，与现有的边界点检测方法相比，该方法是有效的。
In a dataset,boundary pointsare located at the extremes of the clusters. Detecting such boundary points may provide useful information about the process and it can have many real-world applications. Existing methods are sensitive to outliers, clusters of varying densities and require tuning more than one parameter. This paper proposes a boundary point detection method calledBoundary Point Factor (BPF)based on the outlier detection algorithm known as Local Outlier Factor (LOF).BPFcalculates Gravity values and BPF scores by combining original LOF scores of all points in the dataset. Boundary points can be effectively detected by using BPF scores of all points where boundary points tend to have larger BPF scores than other points.BPFrequires tuning of one parameter and it can be used with LOF to output outliers and boundary points separately. Experimental evaluation on synthetic and real datasets showed the effectiveness of our method in comparison with existing boundary points detection methods.