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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:
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>'''的方法通常有两种:
* '''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.
穷举搜索的分裂群集是 < math > mathcal { o }(2 ^ n) </math > ，但是通常使用更快的启发式来选择分裂，比如 k-means。
穷举搜索的分裂群集是 < math > mathcal { o }(2 ^ n) </math > ，但是通常使用更快的启发式来选择分裂，比如 k-means。
== Cluster dissimilarity 簇异性==
== Cluster dissimilarity 簇异性==