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 non-parametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering).
Package details |
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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 | 2.50.0 |
URL | http://www.stat.berkeley.edu/~laan/ http://docpollard.org/ |
Package repository | View on Bioconductor |
Installation |
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