For two clusterings of the same data set, this function calculates the Rand similarity coefficient of the clusterings from the comemberships of the observations. Basically, the comembership is defined as the pairs of observations that are clustered together.
1  rand_indep(labels1, labels2)

labels1 
a vector of 
labels2 
a vector of 
To calculate the Rand index, we compute the 2x2 contingency table, consisting of the following four cells:
the number of observation pairs where both observations are comembers in both clusterings
the number of observation pairs where the observations are comembers in the first clustering but not the second
the number of observation pairs where the observations are comembers in the second clustering but not the first
the number of observation pairs where neither pair are comembers in either clustering
The Rand similarity index is defined as:
R = \frac{n_{11} + n_{00}}{n_{11} + n_{10} + n_{01} + n_{00}}
.
To compute the contingency table, we use the
comembership_table
function.
the Rand index for the two sets of cluster labels
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  ## Not run:
# We generate K = 3 labels for each of n = 10 observations and compute the
# Rand similarity index between the two clusterings.
set.seed(42)
K < 3
n < 10
labels1 < sample.int(K, n, replace = TRUE)
labels2 < sample.int(K, n, replace = TRUE)
rand_indep(labels1, labels2)
# Here, we cluster the \code{\link{iris}} data set with the Kmeans and
# hierarchical algorithms using the true number of clusters, K = 3.
# Then, we compute the Rand similarity index between the two clusterings.
iris_kmeans < kmeans(iris[, 5], centers = 3)$cluster
iris_hclust < cutree(hclust(dist(iris[, 5])), k = 3)
rand_indep(iris_kmeans, iris_hclust)
## End(Not run)

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