adjusted_rand: Computes the adjusted Rand similarity index of two...

Description Usage Arguments Details Value Examples

View source: R/similarity.r

Description

For two clusterings of the same data set, this function calculates the adjusted Rand similarity coefficient of the clusterings from the comemberships of the observations.

Usage

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adjusted_rand(labels1, labels2)

Arguments

labels1

a vector of n clustering labels

labels2

a vector of n clustering labels

Details

The adjusted Rand index is a variant of the Rand index that is corrected for chance. We refer the interested reader to the Wikipedia entry for an overview of the formula: http://en.wikipedia.org/wiki/Rand_index#Adjusted_Rand_index

Value

the adjusted Rand index for the two sets of cluster labels

Examples

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## Not run: 
# We generate K = 3 labels for each of n = 10 observations and compute the
# adjusted Rand 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)
adjusted_rand(labels1, labels2)

# Here, we cluster the \code{\link{iris}} data set with the K-means and
# hierarchical algorithms using the true number of clusters, K = 3.
# Then, we compute the adjusted Rand index between the two clusterings.
iris_kmeans <- kmeans(iris[, -5], centers = 3)$cluster
iris_hclust <- cutree(hclust(dist(iris[, -5])), k = 3)
adjusted_rand(iris_kmeans, iris_hclust)

## End(Not run)

ramhiser/clusteval documentation built on Oct. 17, 2017, 12:26 p.m.