BPF: An Effective Cluster Boundary Points Detection Technique

Abstract

In a dataset, boundary points are 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 called Boundary Point Factor (BPF) based on the outlier detection algorithm known as Local Outlier Factor (LOF). BPF calculates 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. BPF requires 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.