The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. The algorithm uses the Mean/Median Split Silhouette (MSS) criteria to identify the level of the tree with maximally homogeneous clusters. It also runs the tree down to produce a final ordered list of the elements. The nonparametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering).
Package details 


Author  Katherine S. Pollard, with Mark J. van der Laan <laan@stat.berkeley.edu> and Greg Wall 
Bioconductor views  Clustering 
Maintainer  Katherine S. Pollard <katherine.pollard@gladstone.ucsf.edu> 
License  GPL (>= 2) 
Version  2.36.0 
URL  http://www.stat.berkeley.edu/~laan/ http://docpollard.org/ 
Package repository  View on Bioconductor 
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