Algorithms for hierarchical clustering: an overview, II

Fionn Murtagh, Pedro Contreras
2017 Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery  
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 densitybased 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 and Contreras (2012).
doi:10.1002/widm.1219 fatcat:4cdvfpypibe3petriqyuaiunk4