中文摘要：

这篇论文集中于文件由基于 DEnsityTree (CABDET ) 聚类算法改进聚类的精确性聚类。CABDET 方法由动态地根据本地密度调整邻居的半径为每潜在的簇构造基于密度的树结构。它避免与噪音(DBSCAN ) 的应用程序的基于密度的空间聚类的一个的全球密度参数和还原剂输入参数。真实文件的实验的结果证明 CABDET 完成比 DBSCAN 方法聚类的更好的精确性。CABDET 算法获得最大 F 措施与根节点邻居 0.80 的半径珍视 0.347，它比有邻居 0.65 的半径和目标的最小的数字的 0.332 DBSCAN 高 6。

英文摘要：

This paper focuses on document clustering by clustering algorithm based on a DEnsityTree （CABDET） to improve the accuracy of clustering. The CABDET method constructs a density-based treestructure for every potential cluster by dynamically adjusting the radius of neighborhood according to local density. It avoids density-based spatial clustering of applications with noise （DBSCAN） ＇s global density parameters and reduces input parameters to one. The results of experiment on real document show that CABDET achieves better accuracy of clustering than DBSCAN method. The CABDET algorithm obtains the max F-measure value 0.347 with the root node＇s radius of neighborhood 0.80, which is higher than 0.332 of DBSCAN with the radius of neighborhood 0.65 and the minimum number of objects 6.