Description Usage Arguments Details Value Examples

For two clusterings of the same data set, this function calculates the Fowlkes-Mallows 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 | ```
fowlkes_mallows(labels1, labels2)
``` |

`labels1` |
a vector of |

`labels2` |
a vector of |

To calculate the Fowlkes-Mallows index, 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

The Fowlkes-Mallows similarity index is defined as:

*\frac{n_{11}}{√{(n_{11} + n_{10})(n_{11} + n_{01})}}.*

To compute the contingency table, we use the `comembership_table`

function.

the Fowlkes-Mallows 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 19 | ```
## Not run:
# We generate K = 3 labels for each of n = 10 observations and compute the
# Fowlkes-Mallows 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)
fowlkes_mallows(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 Fowlkes-Mallows similarity index between the two
# clusterings.
iris_kmeans <- kmeans(iris[, -5], centers = 3)$cluster
iris_hclust <- cutree(hclust(dist(iris[, -5])), k = 3)
fowlkes_mallows(iris_kmeans, iris_hclust)
## End(Not run)
``` |

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

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