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{{Short description|Stack-based method for clustering}}
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In the theory of [[cluster analysis]], the '''nearest-neighbor chain algorithm''' is an [[algorithm]] that can speed up several methods for [[agglomerative hierarchical clustering]]. These are methods that take a collection of points as input, and create a hierarchy of clusters of points by repeatedly merging pairs of smaller clusters to form larger clusters. The clustering methods that the nearest-neighbor chain algorithm can be used for include [[Ward's method]], [[complete-linkage clustering]], and [[single-linkage clustering]]; these all work by repeatedly merging the closest two clusters but use different definitions of the distance between clusters. The cluster distances for which the nearest-neighbor chain algorithm works are called ''reducible'' and are characterized by a simple inequality among certain cluster distances.
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