hc: Model-based Agglomerative Hierarchical Clustering
In Japrin/mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

Description Usage Arguments Details Value Note References See Also Examples

Agglomerative hierarchical clustering based on maximum likelihood criteria for Gaussian mixture models parameterized by eigenvalue decomposition.

hc(data,
   modelName = mclust.options("hcModelName"),  
   use = mclust.options("hcUse"), ...)

## S3 method for class 'hc'
plot(x, ...)

## S3 method for class 'hc'
as.dendrogram(object, ...)

## S3 method for class 'hc'
as.hclust(x, ...)

`data`	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).
`modelName`	A character string indicating the model to be used. Possible models are: `"E"` equal variance (one-dimensional) `"V"` spherical, variable variance (one-dimensional) `"EII"` spherical, equal volume `"VII"` spherical, unequal volume `"EEE"` ellipsoidal, equal volume, shape, and orientation `"VVV"` ellipsoidal, varying volume, shape, and orientation. By default the model provided by `mclust.options("hcModelName")` is used. See `mclust.options`.
`use`	A string or a vector of character strings specifying the type of input variables/data transformation to be used for model-based hierarchical clustering. By default the method specified in `mclust.options("hcUse")` is used. See `mclust.options`.
`...`	Arguments for the method-specific `hc` functions. See for example `hcE`.
`object, x`	An object of class `'hc'` resulting from a call to `hc()`.

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 "EEE", do not admit a fast algorithm under the usual agglomerative hierarchical clustering paradigm. These use less memory but are much slower to execute.

The function 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 as attributes.

The plotting function plot.hc() draws a dendrogram by first converting the input object from class 'hc' to class 'dendrogram' and then plot the transformed object using plot.dendrogram.

The functions as.dendrogram.hc() and as.hclust.hc() are used to convert the input object from class 'hc' to class, respectively, 'dendrogram' and 'hclust'.

If modelName = "E" (univariate with equal variances) or modelName = "EII" (multivariate with equal spherical covariances), then the method is equivalent to 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.

hcE,..., hcVVV, hclass, mclust.options

hcTree <- hc(modelName = "VVV", data = iris[,-5])
cl <- hclass(hcTree,c(2,3))

## Not run: 
par(pty = "s", mfrow = c(1,1))
clPairs(iris[,-5],cl=cl[,"2"])
clPairs(iris[,-5],cl=cl[,"3"])

par(mfrow = c(1,2))
dimens <- c(1,2)
coordProj(iris[,-5], dimens = dimens, classification=cl[,"2"])
coordProj(iris[,-5], dimens = dimens, classification=cl[,"3"])

## End(Not run)