Hierarchical clustering

In <a href="/facts/Data_mining/AH53q5ac">data mining</a> and <a href="/facts/Statistics/DNPsKGYU">statistics</a>, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of <a href="/facts/Cluster_analysis/HUeDnWPt">cluster analysis</a> that seeks to build a <a href="/facts/Hierarchy/mYJyqFrG">hierarchy</a> of clusters. Strategies for hierarchical clustering generally fall into two categories:

<ul><li>Agglomerative: This is a "<a href="/facts/Top-down_and_bottom-up_design/MvtTkA3x">bottom-up</a>" approach: Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.</li>
<li>Divisive: This is a "<a href="/facts/Top-down_and_bottom-up_design/MvtTkA3x">top-down</a>" approach: All observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.</li></ul>
In general, the merges and splits are determined in a <a href="/facts/Greedy_algorithm/alfsekco">greedy</a> manner. The results of hierarchical clustering are usually presented in a <a href="/facts/Dendrogram/FI8EMSEv">dendrogram</a>.
Hierarchical clustering has the distinct advantage that any valid measure of distance can be used. In fact, the observations themselves are not required: all that is used is a <a href="/facts/Distance_matrix/2ptk07Y7">matrix of distances</a>. On the other hand, except for the special case of single-linkage distance, none of the algorithms (except exhaustive search in 
 
 
 
 
 
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Hierarchical clustering open-in-new

Hierarchical clustering