Algorithm to process hierarchical clustering on individuals (from a matrix individuals x variables). The dissimilarity used in the observation space is the log-likelihood ratio, i.e. D(i,j) = log f(Y_i) + log f(Y_j) - log f(Y_i U Y_j) where Y_i is the vector of observations from individual i. The statistical model defining the considered likelihood is standard Gaussian or Zero-Inflated Gaussian (i.e. a Gaussian-Bernoulli mixture). The agglomeration method is also the log-likelihood ratio, however between clusters in this case.
|Author||Ghislain Durif <[email protected]>, Franck Picard <[email protected]>|
|Date of publication||2016-05-03 09:28:51|
|Maintainer||Ghislain Durif <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on R-Forge|
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