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WIREs Data Mining Knowl Discov
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Algorithms for hierarchical clustering: an overview, II

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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. This review adds to the earlier version, Murtagh F, Contreras P. Algorithms for hierarchical clustering: an overview, Wiley Interdiscip Rev: Data Mining Knowl Discov 2012, 2, 86–97. WIREs Data Mining Knowl Discov 2017, 7:e1219. doi: 10.1002/widm.1219

Five points, showing nearest neighbors and reciprocal nearest neighbors.
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The 77 successive scenes of the Casablanca movie. It shows up scenes 9–10, and progressing from 39–40 and 41, as major changes.
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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 ‘cross over’ or inversion (right panel) arises.
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Algorithms for hierarchical clustering: an overview

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

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