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WeightedCluster (version 1.8-0)

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. Finally, it provides a fuzzy and crisp CLARA algorithm to cluster large database with sequence analysis.

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Version

Install

install.packages('WeightedCluster')

Monthly Downloads

1,124

Version

1.8-0

License

GPL (>= 2)

Maintainer

Matthias Studer

Last Published

October 2nd, 2024

Functions in WeightedCluster (1.8-0)

seqnull

Generate nonclustered sequence data according to different null models.
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
bootclustrange

Cluster Quality Indices estimation by subsampling
wcKMedRange

Compute wcKMedoids clustering for different number of clusters.
clustassoc

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

Plot of cluster quality of CLARA algorithm.
wcKMedoids

K-Medoids or PAM clustering of weighted data.
fuzzyseqplot

Plot sequences according to a fuzzy clustering.
seqclararange

CLARA Clustering for Sequence Analysis
wcSilhouetteObs

Compute the silhouette of each object using weighted data.
wcCmpCluster

Automatic comparison of clustering methods.
wcAggregateCases

Aggregate identical cases.
seqclustname

Automatic labeling of cluster using sequence medoids
seqnullcqi

Sequence Analysis Typologies Validation Using Parametric Bootstrap
seqpropclust

Monothetic clustering of state sequences