基于统计信息的聚类边界模式检测算法

为有效地检测聚类的边界点，提出基于统计信息的边界模式检测算法。根据数据对象的k距离统计信息设定邻域半径，再利用对象邻域范围内邻居的k距离统计信息寻找边界点。实验结果表明，该算法可以有效地检测出任意形状、不同大小和不同密度聚类的边界点，并可以消除噪声。

Boundary Pattern Detection of Clusters Based on Statistics Information

This paper proposes an algorithm named boundary pattern detection based on statistics information to detect boundary points of clusters effectively. BOURN sets neighborhood radius based on the k-dist statistics information of the objects, and searches boundary points based on the k-dist statistics information of neighbors in the neighborhood around it. Experimental results show that BOURN can find boundary points of clusters of arbitrary shapes, different sizes and different density, and can remove noise effectively.