Agglomerative hierarchical clustering based on maximum likelihood criteria for Gaussian mixture models parameterized by eigenvalue decomposition.
hc(data, modelName = mclust.options("hcModelName"), partition, minclus = 1, ..., use = mclust.options("hcUse"))
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations (\(n\)) and columns correspond to variables (\(d\)).
A character string indicating the model to be used.
A numeric or character vector representing a partition of
observations (rows) of
A number indicating the number of clusters at which to stop the agglomeration. The default is to stop when all observations have been merged into a single cluster.
Arguments for the method-specific
A string or a vector of character strings specifying the type of input
variables/data transformation to be used for model-based hierarchical
hc() returns a numeric two-column matrix in which
the ith row gives the minimum index for observations in each of
the two clusters merged at the ith stage of agglomerative
hierarchical clustering. Several other informations are also returned
The plotting method
plot.hc() draws a dendrogram, which can be based
on either the classification loglikelihood or the merge level (number of
clusters). For details, see the associated help file.
Most models have memory usage of the order of the square of the
number groups in the initial partition for fast execution.
Some models, such as equal variance or
do not admit a fast algorithm under the usual agglomerative
hierarchical clustering paradigm.
These use less memory but are much slower to execute.
modelName = "E" (univariate with equal variances) or
modelName = "EII" (multivariate with equal spherical
covariances), then underlying model is the same as that for
Ward's method for hierarchical clustering.
J. D. Banfield and A. E. Raftery (1993). Model-based Gaussian and non-Gaussian Clustering. Biometrics 49:803-821.
C. Fraley (1998). Algorithms for model-based Gaussian hierarchical clustering. SIAM Journal on Scientific Computing 20:270-281.
C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.
hcTree <- hc(modelName = "VVV", data = iris[,-5]) hcTree#> Call: #> hc(data = iris[, -5], modelName = "VVV") #> #> Model-Based Agglomerative Hierarchical Clustering #> Model name = VVV #> Use = SVD #> Number of objects = 150#> #> 1 2 #> 49 101table(cl[,"3"])#> #> 1 2 3 #> 49 62 39