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* '''Agglomerative''': This is a "[[Top-down and bottom-up design|bottom-up]]" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
* '''Agglomerative''': This is a "[[Top-down and bottom-up design|bottom-up]]" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
补充翻译：合并：这是一种“自上而下又自下而上/纵向”的方法：每个被观察数据从自己的簇类中开始，当一个被观察数据向上层移动时，成对的簇类被合并。
补充翻译：合并：这是一种“自上而下又自下而上/纵向”的方法：每个被观察数据从自己的簇类中开始，当一个观察组数据向上层移动时，成对的簇类集群被合并。
* '''Divisive''': This is a "[[Top-down and bottom-up design|top-down]]" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
* '''Divisive''': This is a "[[Top-down and bottom-up design|top-down]]" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
补充翻译：分裂：这是一种“自上而下”的方法：所有的被观察数据都从一个簇类集群中开始，当一个簇类向下移动时，整个观察组数据群会递归地执行分割。
In general, the merges and splits are determined in a [[greedy algorithm|greedy]] manner. The results of hierarchical clustering<ref>{{cite book | author=Frank Nielsen | title=Introduction to HPC with MPI for Data Science | year=2016 | publisher=Springer |
In general, the merges and splits are determined in a [[greedy algorithm|greedy]] manner. The results of hierarchical clustering<ref>{{cite book | author=Frank Nielsen | title=Introduction to HPC with MPI for Data Science | year=2016 | publisher=Springer |