网格聚类中的边界处理技术

Border-Processing Technique in Grid-Based Clustering

提出利用限制性 k 近邻和相对密度的概念识别网格聚类边界点的技术,给出网格聚类中的边界处理算法和带边界处理的网格聚类算法(GBCB).实验表明,聚类边界处理技术精度高,能有效地将聚类的边界点和孤立点/噪声数据分离开来.基于该边界处理技术的网格聚类算法 GBCB 能识别任意形状的聚类.由于它只对数据集进行一遍扫描,算法的运行时间是输人数据大小的线性函数,可扩展性好.

In order to improve accuracy of grid - based clustering , a border - processing technique is proposed , Using restricted k nearest neighbors and concept of relative density . The technique enables us to separate cluster＇s border points from outliers or noises accurately. Then, a grid-based clustering algorithm with border processing （GBCB） is developed. Experiment results show high accuracy of recognition of border points. Due to the only one data scan, the GBCB algorithm is very efficient with its run time being linear to the size of the input data set , and can discover arbitrary shapes of clusters and scale well.