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qcluster (version 1.2.1)

Clustering via Quadratic Scoring

Description

Performs tuning of clustering models, methods and algorithms including the problem of determining an appropriate number of clusters. Validation of cluster analysis results is performed via quadratic scoring using resampling methods, as in Coraggio, L. and Coretto, P. (2023) .

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Version

Install

install.packages('qcluster')

Monthly Downloads

138

Version

1.2.1

License

GPL (>= 2)

Maintainer

Luca Coraggio

Last Published

January 13th, 2025

Functions in qcluster (1.2.1)

mbind

Combines Methods Settings
clust2params

Converts Hard Assignment Into Cluster Parameters
gmix

Gaussian Mixture Modelling
mset_pam

Generates Methods Settings for Partitioning Around Medoids (Pam) Clustering
bqs_select

Select Ranked Cluster Solutions by Quadratic Score
plot_clustering

Plot Data With Clustering Information
plot.mbcfit

Plot Fitted Mixture Models
mset_gmix

Generates Methods Settings for Gaussian Mixture Model-Based Clustering
banknote

Swiss Banknotes Data
bqs

Bootstrapping quadratic scores
print.mbcfit

Display Information for Mixture Model Objects
predict.mbcfit

Predict Hard Clustering Assignments for using Mixture Models
mset_user

Generates Clustering Methods Settings for a Prototype Methodology Provided by the User
qscore

Clustering Quadratic Score
print.bqs

Display Information on Bootstrap Quadratic Scores Objects
plot.bqs

Plot (Bootstrap) Quadratic Score Results
mset_kmeans

Generates Methods Settings for K-Means Clustering
bqs_rank

Ranking Clusters Quadratic Scores Estimated Via Boostrap