CAJ | 학술논문

聚类的边界是一种有用的模式，为有效地提取聚类的边界点，提出c-层近邻概念，将c-层近邻应用于二路生成树，能快速计算出每个对象的反向近邻值，从而根据反向近邻值提取聚类的边界。提出的基于二路生成树的边界检测算法（DBMST）在综合数据集和真实数据集的实验结果表明，该算法在含有噪声/孤立点的数据集上，能够快速有效地识别出聚类的边界。
취류적변계시일충유용적모식，위유효지제취취류적변계점，제출c-층근린개념，장c-층근린응용우이로생성수，능쾌속계산출매개대상적반향근린치，종이근거반향근린치제취취류적변계。제출적기우이로생성수적변계검측산법（DBMST）재종합수거집화진실수거집적실험결과표명，해산법재함유조성/고립점적수거집상，능구쾌속유효지식별출취류적변계。
Clustering boundary is a useful pattern.In order to extract the boundary points of cluster effectively,we propose a c-layer nearest neighbour concept,and apply it to two rounds of minimum spanning trees.This novel conception can count every point’s reverse nearest neighbouring value rapidly,thus according to the value the boundary of cluster is extracted.Results of the experiment of proposed boundary detecting algorithm based on two round minimum spanning carried out on both synthesise datasets and real datasets demonstrate that the algorithm can fast identify the boundary of clusters effectively on the datasets with noise/outlier points.