Create a hierarchical structure using a random hierarchical partition of the data.

hcRandomPairs(data, seed = NULL, ...)

Arguments

data

A numeric matrix or data frame of observations. If a matrix or data frame, rows correspond to observations and columns correspond to variables.

seed

Optional single value, interpreted as an integer, specifying the seed for random partition.

...

Catches unused arguments in indirect or list calls via do.call.

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 a random agglomerative hierarchical clustering.

See also

Examples

data <- iris[,1:4] randPairs <- hcRandomPairs(data) str(randPairs)
#> int [1:2, 1:149] 2 16 70 140 76 133 26 106 89 97 ... #> - attr(*, "initialPartition")= int [1:150] 1 2 3 4 5 6 7 8 9 10 ... #> - attr(*, "dimensions")= num [1:2] 150 2
# start model-based clustering from a random partition mod <- Mclust(data, initialization = list(hcPairs = randPairs)) summary(mod)
#> ---------------------------------------------------- #> Gaussian finite mixture model fitted by EM algorithm #> ---------------------------------------------------- #> #> Mclust VVV (ellipsoidal, varying volume, shape, and orientation) model with 2 #> components: #> #> log-likelihood n df BIC ICL #> -214.3547 150 29 -574.0178 -574.0191 #> #> Clustering table: #> 1 2 #> 50 100