Methods of hierarchical clustering
F Murtagh, P Contreras - arXiv preprint arXiv:1105.0121, 2011 - arxiv.org
arXiv preprint arXiv:1105.0121, 2011•arxiv.org
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.
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.
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.
arxiv.org