clusterv (version 1.1.1)

AC.index: Assignment Confidence (AC) index

Description

Assignment confidence index computation. For a given clustering and similarity matrix, the set of AC indices are computed (for each cluster and each example) It assumes that the label of the examples are integers.

Usage

AC.index(cluster, c, Sim.M)

Value

matrix with the Assignment Confidence index for each example. Each row corresponds to an example, each column to a cluster.

Arguments

cluster

list of the clusters whose validity indices will be computed

c

number of clusters

Sim.M

similarity matrix

Author

Giorgio Valentini

Details

The Assignment-Confidence (AC) index estimates the confidence of the assignment of an example i to a cluster A using a similarity matrix M: $$ AC(i,A) = \frac{1}{|A|-1} \sum_{j \in A, j\neq i} M_{ij} $$ Using a set of realizations of a given randomized projection, the AC-index represents the frequency by which i appears with the other elements of the cluster A.

See Also

Validity.indices, Cluster.validity, Cluster.validity.from.similarity,

Do.similarity.matrix.partition, Do.similarity.matrix

Examples

# Computation of the AC indices of a hierarchical clustering algorithm 
M <- generate.sample0(n=10, m=2, sigma=2, dim=800)
d <- dist (t(M)); 
tree <- hclust(d, method = "average");
plot(tree, main="");
cl.orig <- rect.hclust(tree, k = 3);
l.norm <- Multiple.Random.hclustering (M, dim=100, pmethod="Norm", 
                                       c=3, hmethod="average", n=20)
Sim <- Do.similarity.matrix.partition(l.norm);
ac <- AC.index(cl.orig, c=3, Sim)