medv
(Package: mcclust) :
Clustering Method of Medvedovic
Based on a posterior similarity matrix of a sample of clusterings medv obtains a clustering by using 1psm as distance matrix for hierarchical clustering with complete linkage. The dendrogram is cut at a value h close to 1.
● Data Source:
CranContrib
● Keywords: cluster
● Alias: medv
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minbinder
(Package: mcclust) :
Minimize/Compute Posterior Expectation of Binders Loss Function
Based on a posterior similarity matrix of a sample of clusterings minbinder finds the clustering that minimizes the posterior expectation of Binders loss function, while binder computes the posterior expected loss for several provided clusterings.
● Data Source:
CranContrib
● Keywords: cluster, optimize
● Alias: binder, laugreen, minbinder
●
0 images

cltoSim
(Package: mcclust) :
Compute Similarity Matrix for a Clustering and vice versa
A similarity matrix is a symmetric matrix whose entry [i,j] is 1 if observation i and j are in the same cluster and 0 otherwise.
● Data Source:
CranContrib
● Keywords: cluster
● Alias: Simtocl, cltoSim
●
0 images

norm.label
(Package: mcclust) :
Norm Labelling of a Clustering
Cluster labels of a clusterings are replaced by 1:length(table(cl)) .
● Data Source:
CranContrib
● Keywords: cluster
● Alias: norm.label
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Implements methods for processing a sample of (hard) clusterings, e.g. the MCMC output of a Bayesian clustering model. Among them are methods that find a single best clustering to represent the sample, which are based on the posterior similarity matrix or a relabelling algorithm.
● Data Source:
CranContrib
● Keywords: package
● Alias: mcclust, mcclustpackage
●
0 images

arandi
(Package: mcclust) :
(Adjusted) Rand Index for Clusterings
Computes the adjusted or unadjusted Rand index between two clusterings/partitions of the same objects.
● Data Source:
CranContrib
● Keywords: cluster
● Alias: arandi
●
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vi.dist
(Package: mcclust) :
Variation of Information Distance for Clusterings
Computes the 'variation of information' distance of Meila (2007) between two clusterings/partitions of the same objects.
● Data Source:
CranContrib
● Keywords: cluster
● Alias: vi.dist
●
0 images

comp.psm
(Package: mcclust) :
Estimate Posterior Similarity Matrix
For a sample of clusterings of the same objects the proportion of clusterings in which observation i and j are together in a cluster is computed and a matrix containing all proportions is given out.
● Data Source:
CranContrib
● Keywords: cluster
● Alias: comp.psm
●
0 images

relabel
(Package: mcclust) :
Stephens' Relabelling Algorithm for Clusterings
For a sample of clusterings in which corresponding clusters have different labels the algorithm attempts to bring the clusterings to a unique labelling.
● Data Source:
CranContrib
● Keywords: cluster
● Alias: relabel
●
0 images

maxpear
(Package: mcclust) :
Maximize/Compute Posterior Expected Adjusted Rand Index
Based on a posterior similarity matrix of a sample of clusterings maxpear finds the clustering that maximizes the posterior expected Rand adjusted index (PEAR) with the true clustering, while pear computes PEAR for several provided clusterings.
● Data Source:
CranContrib
● Keywords: cluster, optimize
● Alias: maxpear, pear
●
0 images
