基于变异系数的边界点检测算法

为有效检测聚类的边界点,提出基于变异系数的边界点检测算法.首先计算出数据对象到它的k-距离邻居距离之和的平均值.然后用平均值的倒数作为每个点的密度,通过变异系数刻画数据对象密度分布特征寻找边界点.实验结果表明,该算法可在含有任意形状、不同大小和不同密度的数据集上快速有效检测出聚类的边界点,并可消除噪声.

Boundary Points Detection Algorithm Based on Coefficient of Variation

In order to detect boundary points of clusters effectively, an algorithm is proposed, namely boundary points detecting algorithm based on coefficient of variation (BAND). BAND computes the average distance between one object and its k-distance neighbors. The density of each object is obtained by the reciprocal of average distance. Then the boundary points are found by using the coefficient of variation to portray the distribution of data objects. The experimental results show BAND effectively detects boundary points on noisy datasets with clusters of arbitrary shapes, sizes and different densities.