R: Model-based Hierarchical Clustering

hc {mclust} | R Documentation

## Model-based Hierarchical Clustering

### Description

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

### Usage

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

### Arguments

`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 and columns correspond to variables.
`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`.

### Details

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.

### Value

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.

### Note

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.

### References

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.

### See Also

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

### Examples

    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)

[Package mclust version 5.4.2 Index]