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第17行：
第17行： − 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：分离：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中开始，当一个簇类向下移动时，整个数据群会递归地执行分割。+ 第27行：
第27行： − 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 | + + 第33行：
第34行： − 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。+
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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。
* '''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中开始，当一个簇类向下移动时，整个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 |
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 |
此处翻译编辑视图内有显示阅读视图中无。
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.
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。