噪声数据集上的边界点检测算法

Boundary Points Detecting Algorithm for Clusters in Noisy Dataset

为了有效检测聚类的边界点,提出了结合对象的密度及其Eps-邻域中数据的分布特点进行的边界点检测技术和边界点检测算法——BOUND。实验结果表明,BOUND能在含有不同形状、大小簇的噪声数据集上有效地检测出聚类的边界点,并且执行效率高。

In order to detect boundary points of clusters effectively,a technique making use of objects’ density and distribution feature in its Eps-neighborhood to detect boundary points,and a boundary points detecting algorithm named BOUND（detecting boundary points of clusters in noisy dataset） is developed.Experimental results show that BOUND can detect boundary points in noisy dataset containing different shapes and sizes clusters effectively and efficiently.