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A category of clustering algorithms that create a tree of clusters. Hierarchical clustering is well-suited to hierarchical data, such as botanical taxonomies. There are two types of hierarchical clustering algorithms:
Agglomerative clustering first assigns every example to its own cluster, and iteratively merges the closest clusters to create a hierarchical tree.
Divisive clustering first groups all examples into one cluster and then iteratively divides the cluster into a hierarchical tree.
Contrast with centroid-based clustering.
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