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WeightedCluster (version 1.6-4)

Clustering of Weighted Data

Description

Clusters state sequences and weighted data. It provides an optimized weighted PAM algorithm as well as functions for aggregating replicated cases, computing cluster quality measures for a range of clustering solutions and plotting (fuzzy) clusters of state sequences. Parametric bootstraps methods to validate typology of sequences are also provided.

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Version

Install

install.packages('WeightedCluster')

Monthly Downloads

958

Version

1.6-4

License

GPL (>= 2)

Maintainer

Matthias Studer

Last Published

July 7th, 2023

Functions in WeightedCluster (1.6-4)

seqclustname

Automatic labeling of cluster using sequence medoids
wcAggregateCases

Aggregate identical cases.
seqnull

Generate nonclustered sequence data according to different null models.
fuzzyseqplot

Plot sequences according to a fuzzy clustering.
wcKMedoids

K-Medoids or PAM clustering of weighted data.
wcCmpCluster

Automatic comparison of clustering methods.
wcSilhouetteObs

Compute the silhouette of each object using weighted data.
clustassoc

Share of an association between an object (described by a dissimilarity matrix) and a covariate that is reproduced by a clustering solution.
wcKMedRange

Compute wcKMedoids clustering for different number of clusters.
as.clustrange

Build a clustrange object to compare different clustering solutions.
as.seqtree

Convert a hierarchical clustering object to a seqtree object.
wcClusterQuality

Cluster quality statistics
seqnullcqi

Sequence Analysis Typologies Validation Using Parametric Bootstrap
seqpropclust

Monothetic clustering of state sequences