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("hcModelNames")[1],
       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 first model listed in `mclust.options("hcModelNames")`, i.e. `"VVV"`, is used.
`use` | A character string specifying what type of data/transformation should be used for model-based hierarchical clustering. This is experimental and it is only useful for the initialization of EM algorithm. By default it uses the method specified in `mclust.options("hcUse")`, which is set to `"VARS"`, i.e. the original input variables.
`...` | 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.

### 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.

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

### 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.

### 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 5.3 Index]