R: Model-based Hierarchical Clustering

hcE {mclust} | R Documentation

## Model-based Hierarchical Clustering

### Description

Agglomerative hierarchical clustering based on maximum likelihood for a Gaussian mixture model parameterized by eigenvalue decomposition.

### Usage

    hcE(data, partition = NULL, minclus=1, ...)
    hcV(data, partition = NULL, minclus = 1, alpha = 1, ...)
    hcEII(data, partition = NULL, minclus = 1, ...)
    hcVII(data, partition = NULL, minclus = 1, alpha = 1, ...)
    hcEEE(data, partition = NULL, minclus = 1, ...)
    hcVVV(data, partition = NULL, minclus = 1, alpha = 1, beta = 1, ...)

### 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.
`partition` | A numeric or character vector representing a partition of observations (rows) of `data`. If provided, group merges will start with this partition. Otherwise, each observation is assumed to be in a cluster by itself at the start of agglomeration.
`minclus` | 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.
`alpha, beta` | Additional tuning parameters needed for initializatiion in some models. For details, see Fraley 1998. The defaults provided are usually adequate.
`...` | Catch unused arguments from a `do.call` call.

### 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 hierachical 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.

### See Also

`hc`, `hclass` `hcRandomPairs`

### Examples

    hcTree <- hcEII(data = iris[,-5])
    cl <- hclass(hcTree,c(2,3))

    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], classification=cl[,"2"], dimens=dimens)
    coordProj(iris[,-5], classification=cl[,"3"], dimens=dimens)

[Package mclust version 5.4.9 Index]