T4cluster (version 0.1.2)

psm: Compute Posterior Similarity Matrix

Description

Let clustering be a label from data of \(N\) observations and suppose we are given \(M\) such labels. Posterior similarity matrix, as its name suggests, computes posterior probability for a pair of observations to belong to the same cluster, i.e., $$P_{ij} = P(\textrm{label}(X_i) = \textrm{label}(X_j))$$ under the scenario where multiple clusterings are samples drawn from a posterior distribution within the Bayesian framework. However, it can also be used for non-Bayesian settings as psm is a measure of uncertainty embedded in any algorithms with non-deterministic components.

Usage

psm(partitions)

Arguments

partitions

partitions can be provided in either (1) an \((M\times N)\) matrix where each row is a clustering for \(N\) objects, or (2) a length-\(M\) list of length-\(N\) clustering labels.

Value

an \((N\times N)\) matrix, whose elements \((i,j)\) are posterior probability for an observation \(i\) and \(j\) belong to the same cluster.

See Also

pcm

Examples

# NOT RUN {
# -------------------------------------------------------------
#               PSM with 'iris' dataset + k-means++
# -------------------------------------------------------------
## PREPARE WITH SUBSET OF DATA
data(iris)
X     = as.matrix(iris[,1:4])
lab   = as.integer(as.factor(iris[,5]))

## EMBEDDING WITH PCA
X2d = Rdimtools::do.pca(X, ndim=2)$Y

## RUN K-MEANS++ 100 TIMES
partitions = list()
for (i in 1:100){
  partitions[[i]] = kmeanspp(X)$cluster
}

## COMPUTE PSM
iris.psm = psm(partitions)

## VISUALIZATION
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2), pty="s")
plot(X2d, col=lab, pch=19, main="true label")
image(iris.psm[,150:1], axes=FALSE, main="PSM")
par(opar)

# }