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 <[email protected]> and Greg Wall|
|Maintainer||Katherine S. Pollard <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on GitHub|
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