In data mining, hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types:
- Agglomerative: This is a "bottom up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
- Divisive: This is a "top down" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
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, which makes them too slow for large data sets. Divisive clustering with an exhaustive search is, which is even worse. However, for some special cases, optimal efficient agglomerative methods (of complexity ) are known: SLINK for single-linkage and CLINK for complete-linkage clustering.
Read more about Hierarchical Clustering: Cluster Dissimilarity, Discussion, Example For Agglomerative Clustering
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“Authority is the spiritual dimension of power because it depends upon faith in a system of meaning that decrees the necessity of the hierarchical order and so provides for the unity of imperative control.”
—Shoshana Zuboff (b. 1951)