Canopy聚类算法是一个将对象分组到类的简单、快速、精确地方法。每个对象用多维特征空间里的一个点来表示。这个算法使用一个快速近似距离度量和两个距离阈值 T1>T2来处理。 Canopy聚类算法能快速找出应该选择多少个簇，同时找到簇的中心，这样可以大大优化 K均值聚类算法的效率 。-Canopy is a clustering algorithm to group objects into simple categories, fast, accurate method. Each object with a multi-dimensional feature space of a point to represent. The algorithm uses a fast approximate distance metric and two distance threshold T1> T2 to deal with. Canopy clustering algorithm to quickly identify how many clusters should be chosen, while finding the center of the cluster, which can greatly optimize the efficiency of the K-means clustering algorithm.