Implements a constrained version of hierarchical agglomerative
clustering, in which each observation is associated to a position, and
only adjacent clusters can be merged. Typical application fields in
bioinformatics include Genome-Wide Association Studies or Hi-C data
analysis, where the similarity between items is a decreasing function of
their genomic distance. Taking advantage of this feature, the implemented
algorithm is time and memory efficient. This algorithm is described in
Chapter 4 of Alia Dehman (2015)
|Maintainer||Pierre Neuvial <[email protected]>|
|Package repository||View on GitHub|
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