Description Usage Arguments Details Value Examples

For two clusterings of the same data set, this function calculates the Dice 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 | ```
dice(labels1, labels2)
``` |

`labels1` |
a vector of |

`labels2` |
a vector of |

To calculate the Dice 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 Dice similarity index is defined as:

*\frac{2 * n_{11}}{2 n_{11} + n_{10} + n_{01}}.*

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

function.

the Dice 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
# Dice 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)
dice(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 Dice similarity index between the two clusterings.
iris_kmeans <- kmeans(iris[, -5], centers = 3)$cluster
iris_hclust <- cutree(hclust(dist(iris[, -5])), k = 3)
dice(iris_kmeans, iris_hclust)
## End(Not run)
``` |

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

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