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In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types:

In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types:

在数据挖掘和统计学中，'''<font color="#ff8000"> 层次聚类Hierarchical clustering</font>'''(也称为层次数据聚类聚类或 HCA)是一种数据聚类的方法，它旨在建立一个集群层次结构。'''<font color="#ff8000"> 层次聚类Hierarchical clustering</font>'''的策略通常分为两类:

在数据挖掘和统计学中，'''<font color="#ff8000"> 层次聚类Hierarchical clustering</font>'''(也被称为层次数据聚类或HCA)是一种数据聚类的方法，它旨在建立一个集群层次结构。'''<font color="#ff8000"> 实现层次聚类Hierarchical clustering</font>'''的策略通常有两种:

* '''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.