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Algorithms for hierarchical clustering: an overview

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Abstract We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self‐organizing maps, and mixture models. We review grid‐based clustering, focusing on hierarchical density‐based approaches. Finally, we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid‐based algorithm. © 2011 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Hierarchies and Trees Technologies > Structure Discovery and Clustering

Five points, showing nearest neighbors and reciprocal nearest neighbors.

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Alternative representations of a hierarchy with an inversion. Assuming dissimilarities, as we go vertically up, agglomerative criterion values (d1, d2) increase so that d2 > d1. But here, undesirably, d2 < d1 and the “crossover” or inversion (right panel) arises.

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Algorithmic Development > Hierarchies and Trees
Technologies > Structure Discovery and Clustering

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