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hopach (version 2.32.0)

Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH)

Description

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).

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Version

Monthly Downloads

205

Version

2.32.0

License

GPL (>= 2)

Homepage

http://www.stat.berkeley.edu/~laan/

Maintainer

Katherine S Pollard

Last Published

February 15th, 2017

Functions in hopach (2.32.0)

function to write MapleTree files for viewing bootstrap estimated cluster membership probabilities based on hopach clustering results

hopach-internal

Functions used internally by the hopach package

Functions to compute pair-wise distances

function to write a text file with hopach output

function to write MapleTree files for viewing hopach hierarchical clustering results

functions to perform non-parametric bootstrap resampling of hopach clustering results

correlationordering

function to compute empirical correlation between distance in a list and distance by a metric

Class "hdist" - S4 class to hold distance matrices.

function to prune a HOPACH tree.

Functions to compute silhouettes and split silhouettes

Gene expression dataset from Golub et al. (1999)

function to perform HOPACH hierarchical clustering

function to make a pseudo-color image of a distance matrix with the row and column ordering based on HOPACH clustering results.

function to make a barplot of bootstrap estimated cluster membership probabilities

functions to compute pair wise distances between vectors