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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：“聚集”：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，当一个被观察数据向上层移动时，成对的簇类被合并。

* '''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.

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 |

In general, the merges and splits are determined in a 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 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 |

一般来说，合并和分裂是以贪婪的方式决定的。'''<font color="#ff8000"> 层次聚类Hierarchical clustering</font>'''的结果 < ref > { cite book | author = Frank Nielsen | title = Introduction to HPC with MPI for Data Science | year = 2016 | publisher = Springer |  

一般来说，合并和分裂是以使用者希望的方式决定的。'''<font color="#ff8000"> 而层次聚类Hierarchical clustering</font>'''的结果 < ref > { cite book | author = Frank Nielsen | title = Introduction to HPC with MPI for Data Science | year = 2016 | publisher = Springer |  

chapter=Chapter 8: Hierarchical Clustering | url=https://www.springer.com/gp/book/9783319219028 |chapter-url=https://www.researchgate.net/publication/314700681 }}</ref> are usually presented in a [[dendrogram]].

chapter=Chapter 8: Hierarchical Clustering | url=https://www.springer.com/gp/book/9783319219028 |chapter-url=https://www.researchgate.net/publication/314700681 }}</ref> are usually presented in a [[dendrogram]].

chapter=Chapter 8: Hierarchical Clustering | url=https://www.springer.com/gp/book/9783319219028 |chapter-url=https://www.researchgate.net/publication/314700681 }}</ref> are usually presented in a dendrogram.

chapter=Chapter 8: Hierarchical Clustering | url=https://www.springer.com/gp/book/9783319219028 |chapter-url=https://www.researchgate.net/publication/314700681 }}</ref> are usually presented in a dendrogram.

第八章: '''<font color="#ff8000"> 层次聚类Hierarchical clustering</font>''' | url =  https://www.springer.com/gp/book/9783319219028 | Chapter-url =  https://www.researchgate.net/publication/314700681} </ref > 通常在树状图中呈现。

正如在第八章: '''<font color="#ff8000"> 层次聚类Hierarchical clustering</font>''' | url =  https://www.springer.com/gp/book/9783319219028 | Chapter-url =  https://www.researchgate.net/publication/314700681} </ref > 中所言，通常在树状图中呈现。