cer {SparseFunClust}

CER function

Description

Given two partitions P and Q, cer(P, Q) measures how well they agree, the lower the better. It is rigorously defined as the proportion of pairwise disagreements in the two partitions (i.e., how many, out of all the possible couples of elements in the sample, are localized in the same cluster in one partition and in a different one in the other partition).

Usage

cer(P, Q)

Arguments

P

first vector of cluster assignments (length n)

Q

second vector of cluster assignments (length n)

Value

The CER index, which is a number between 0 and 1, and also equal to 1 - Rand index (Rand, 1971), a popular measure of the goodness of a clustering.

References

Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical association, 66(336), 846-850.

Examples

set.seed(8988327)
x <- seq(0, 1, len = 500)
out <- generate.data.FV17(50, x)
result <- SparseFunClust(out$data, x, K = 2, do.alignment = FALSE)
cer(out$true.partition, result$labels)