Algorithms for hierarchical clustering: an overview, <scp>II</scp>

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<jats:p>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, <jats:italic>Wiley Interdiscip Rev: Data Mining Knowl Discov</jats:italic> 2012, 2, 86–97. <jats:italic>WIREs Data Mining Knowl Discov</jats:italic> 2017, 7:e1219. doi: 10.1002/widm.1219</jats:p><jats:p>This article is categorized under: <jats:list list-type="explicit-label"> <jats:list-item><jats:p>Algorithmic Development > Hierarchies and Trees</jats:p></jats:list-item> <jats:list-item><jats:p>Technologies > Classification</jats:p></jats:list-item> <jats:list-item><jats:p>Technologies > Structure Discovery and Clustering</jats:p></jats:list-item> </jats:list></jats:p>

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