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).
| Version: | 2.6.0 |
| Depends: | R (≥ 2.6.0), cluster, Biobase, methods |
| Published: | 2009-11-13 |
| Author: | Katherine S. Pollard, with Mark J. van der Laan and Greg Wall |
| Maintainer: | Katherine S. Pollard <katherine.pollard at gladstone.ucsf.edu> |
| License: | GPL (≥ 2) |
| URL: | http://www.stat.berkeley.edu/~laan/, http://docpollard.com/ |
| In views: | Cluster, Multivariate |
| CRAN checks: | hopach results |
| Package source: | hopach_2.6.0.tar.gz |
| MacOS X binary: | hopach_2.6.0.tgz |
| Windows binary: | hopach_2.6.0.zip |
| Reference manual: | hopach.pdf |
| Vignettes: |
hopach |
| Old sources: | hopach archive |