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).
|Author||Katherine S. Pollard, with Mark J. van der Laan <firstname.lastname@example.org> and Greg Wall|
|Date of publication||None|
|Maintainer||Katherine S. Pollard <email@example.com>|
|License||GPL (>= 2)|
boot2fuzzy: function to write MapleTree files for viewing bootstrap...
bootplot: function to make a barplot of bootstrap estimated cluster...
bootstrap: functions to perform non-parametric bootstrap resampling of...
correlationordering: function to compute empirical correlation between distance in...
disscosangle: Functions to compute pair-wise distances
distancematrix: functions to compute pair wise distances between vectors
dplot: function to make a pseudo-color image of a distance matrix...
golub: Gene expression dataset from Golub et al. (1999)
hdist-class: Class "hdist" - S4 class to hold distance matrices.
hopach: function to perform HOPACH hierarchical clustering
hopach2tree: function to write MapleTree files for viewing hopach...
hopach.internal: Functions used internally by the hopach package
labelstomss: Functions to compute silhouettes and split silhouettes
makeoutput: function to write a text file with hopach output
prune: function to prune a HOPACH tree.