misclassification: Computes misclassification rate
In psobczyk/public_varclust: Variable Clustering

Description Usage Arguments Details Value References Examples

Missclasification is a commonly used performance measure in subspace clustering. It allows to compare two partitions with the same number of clusters.

1	misclassification(group, true_group, M, K)

`group`	a vector, first partition
`true_group`	a vector, second (reference) partition
`M`	an integer, maximal number of elements in one class
`K`	an integer, number of classes

As getting exact value of misclassification requires checking all permutations and is therefore intrackable even for modest number of clusters, a heuristic approach is proposed. It is assumed that there are K classes of maximum M elements. Additional requirement is that classes labels are from range [1, K].

misclassification rate

R. Vidal. Subspace clustering. Signal Processing Magazine, IEEE, 28(2):52-68,2011

sim.data <- data.simulation(n = 100, SNR = 1, K = 5, numb.vars = 30, max.dim = 2)
mlcc.fit <- mlcc.reps(sim.data$X, numb.clusters = 5, numb.runs = 20, max.dim = 2)
misclassification(mlcc.fit$segmentation,sim.data$s, 30, 5)


#one can use this function not only for clusters
partition1 <- sample(10, 300, replace = TRUE)
partition2 <- sample(10, 300, replace = TRUE)
misclassification(partition1, partition1, max(table(partition1)), 10)
misclassification(partition1, partition2, max(table(partition2)), 10)