# cluster_similarity

0th

Percentile

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

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
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

##### Aliases
• cluster_similarity
##### Examples
# 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)

Documentation reproduced from package clusteval, version 0.1, License: MIT

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