Abstract: With the wide application of categorical-attribute dataset, the demand of obtaining the cluster boundary of categorical-attribute dataset becomes more and more urgent. In order to get cluster boundaries, the individual proposed a categorical-attribute data boundary detection algorithm: CBORDER(Categorical dataset BORDER detection algorithm) In this algorithm, firstly, initializing the center of cluster by using random allocation and utilizing boundary-degree to partition clusters, at the same time, we get the evidence of capturing boundary recorders. Then, basing on the evidence accumulation, we execute the above procedure repeatedly to acquire the boundaries of clusters at the end. Multi-experimental results demonstrate that CBORDER detect boundaries of the high dimension categorical data efficiently.

Key words: boundary-degree, evidence accumulation, boundary of cluster, categorical dataset

摘要： 随着分类属性数据集的应用越来越广泛，获取含有分类属性数据集的聚类边界的需求也越来越迫切。为了获取聚类的边界，在定义分类数据的边界度和聚类边界的基础上，提出了一种带分类属性数据的聚类边界检测算法——CBORDER。该算法首先利用随机分配初始聚类中心和边界度对类进行划分并获取记录边界点的证据，然后运用证据积累的思想多次执行该过程来获取聚类的边界。实验结果表明，CBORDER算法能有效地检测出高维分类属性数据集中聚类的边界。

关键词: 边界度, 证据积累, 聚类边界, 分类数据