Description Usage Arguments Value See Also Examples
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.
1 | psm(partitions)
|
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. |
an (N\times N) matrix, whose elements (i,j) are posterior probability for an observation i and j belong to the same cluster.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | # -------------------------------------------------------------
# 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)
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