hc {mclust} | R Documentation |
Agglomerative hierarchical clustering based on maximum likelihood criteria for MVN mixture models parameterized by eigenvalue decomposition.
hc(modelName, data, ...)
modelName |
A character string indicating the model. Possible models: "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 |
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. |
... |
Arguments for the method-specific hc functions. See
hcE .
|
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.
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.
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. See http://www.stat.washington.edu/mclust.
C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust.
C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.
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.
data(iris) irisMatrix <- as.matrix(iris[,1:4]) hcTree <- hc(modelName = "VVV", data = irisMatrix) cl <- hclass(hcTree,c(2,3)) par(pty = "s", mfrow = c(1,1)) clPairs(irisMatrix,cl=cl[,"2"]) clPairs(irisMatrix,cl=cl[,"3"]) par(mfrow = c(1,2)) dimens <- c(1,2) coordProj(irisMatrix, classification=cl[,"2"], dimens=dimens) coordProj(irisMatrix, classification=cl[,"3"], dimens=dimens)