Computes the similarity between two clusterings of the same data set.

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Description

For two clusterings of the same data set, this function calculates the similarity statistic specified of the clusterings from the comemberships of the observations. Basically, the comembership is defined as the pairs of observations that are clustered together.

Usage

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  cluster_similarity(labels1, labels2,
    similarity = c("jaccard", "rand"),
    method = "independence")

Arguments

labels1

a vector of n clustering labels

labels2

a vector of n clustering labels

similarity

the similarity statistic to calculate

method

the model under which the statistic was derived

Details

To calculate the similarity, we compute the 2x2 contingency table, consisting of the following four cells:

n_11

the number of observation pairs where both observations are comembers in both clusterings

n_10

the number of observation pairs where the observations are comembers in the first clustering but not the second

n_01

the number of observation pairs where the observations are comembers in the second clustering but not the first

n_00

the number of observation pairs where neither pair are comembers in either clustering

Currently, we have implemented the following similarity statistics:

  • Rand index

  • Jaccard coefficient

To compute the contingency table, we use the comembership_table function.

Value

the similarity between the two clusterings

Examples

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# Notice that the number of comemberships is 'n choose 2'.
iris_kmeans <- kmeans(iris[, -5], centers = 3)$cluster
iris_hclust <- cutree(hclust(dist(iris[, -5])), k = 3)
cluster_similarity(iris_kmeans, iris_hclust)