Description Usage Arguments Details Value Examples

For two clusterings of the same data set, this function calculates the Rogers-Tanimoto 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 | ```
rogers_tanimoto(labels1, labels2)
``` |

`labels1` |
a vector of |

`labels2` |
a vector of |

To calculate the Rogers-Tanimoto 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

The Rogers-Tanimoto similarity is defined as:

*\frac{n_{11} + n_{00}}{n_{11} + 2 (n_{10} + n_{01}) + n_{00}}.*

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

function.

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

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