Hierarchical clustering

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 general, the merges and splits are determined in a greedy manner. The results of hierarchical clustering are usually presented in a dendrogram.

In the general case, the complexity of agglomerative clustering is ${\displaystyle O(n^{2}\log(n))}$, which makes them too slow for large data sets. Divisive clustering with an exhaustive search is ${\displaystyle O(2^{n})}$, which is even worse. However, for some special cases, optimal efficient agglomerative methods (of complexity ${\displaystyle O(n^{2}))}$) are known: SLINK for single-linkage and CLINK for complete-linkage clustering.

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