支持k-离群度的边界点检测方法  

Boundary detection method in support of k-outlier degree

作　　者：王桂芝[1] 李井竹[1] 狄志超[2] 

机构地区：[1]河南商业高等专科学校计算机应用系,郑州450044 [2]郑州大学信息工程学院,郑州450052

出　　处：《计算机工程与应用》2011年第33期140-142,共3页Computer Engineering and Applications

基　　金：河南省高等学校青年骨干教师资助计划项目(No.2010GGJS-200)

摘　　要：边界是一种有用的模式,为了有效识别边界,根据边界点周围密度不均匀,提出了一种边界点检测算法——BDKD。该算法用数据对象的k-近邻距离与其邻域内数据对象的平均k-近邻距离之比定义其k-离群度,当k-离群度超过阈值时即确定为边界点。实验结果表明,BDKD算法可以准确检测出各种聚类边界,并能去除噪声,特别是对密度均匀的数据集效果理想。Border is a useful model.In order to detect the boundaries of various shapes effectively,based on the uneven density around the border point,this paper proposes a boundary detection algorithm—BDKD.This algorithm defines the ratio of k-nearest neighbor distance of data objects to the average neighborhood of them as their k-outlier degrees.They are identified as boundary points when the k-outlier of them exceeds the threshold.The results show that BDKD algorithm can accurately detect the boundaries of various clusters and remove the noises.In particular,BDKD algorithm is suitable for the data set of uniform density satisfactorily.

关 键 词：聚类 边界点 k-近邻距离 k-离群度 边界因子 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]