Package: anticlust 0.8.7
anticlust: Subset Partitioning via Anticlustering
The method of anticlustering partitions a pool of elements into groups (i.e., anticlusters) with the goal of maximizing between-group similarity or within-group heterogeneity. The anticlustering approach thereby reverses the logic of cluster analysis that strives for high within-group homogeneity and clear separation between groups. Computationally, anticlustering is accomplished by maximizing instead of minimizing a clustering objective function, such as the intra-cluster variance (used in k-means clustering) or the sum of pairwise distances within clusters. The main function anticlustering() gives access to optimal and heuristic anticlustering methods described in Papenberg and Klau (2021; <doi:10.1037/met0000301>), Brusco et al. (2020; <doi:10.1111/bmsp.12186>), and Papenberg (2024; <doi:10.1111/bmsp.12315>). The optimal algorithms require that an integer linear programming solver is installed. This package will install 'lpSolve' (<https://cran.r-project.org/package=lpSolve>) as a default solver, but it is also possible to use the package 'Rglpk' (<https://cran.r-project.org/package=Rglpk>), which requires the GNU linear programming kit (<https://www.gnu.org/software/glpk/glpk.html>), or the package 'Rsymphony' (<https://cran.r-project.org/package=Rsymphony>), which requires the SYMPHONY ILP solver (<https://github.com/coin-or/SYMPHONY>). 'Rglpk' and 'Rsymphony' have to be manually installed by the user because they are only "suggested" dependencies. Full access to the bicriterion anticlustering method proposed by Brusco et al. (2020) is given via the function bicriterion_anticlustering(), while kplus_anticlustering() implements the full functionality of the k-plus anticlustering approach proposed by Papenberg (2024). Some other functions are available to solve classical clustering problems. The function balanced_clustering() applies a cluster analysis under size constraints, i.e., creates equal-sized clusters. The function matching() can be used for (unrestricted, bipartite, or K-partite) matching. The function wce() can be used optimally solve the (weighted) cluster editing problem, also known as correlation clustering, clique partitioning problem or transitivity clustering.
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anticlust.pdf |anticlust.html✨
anticlust/json (API)
# Install 'anticlust' in R: |
install.packages('anticlust', repos = c('https://m-py.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/m-py/anticlust/issues
- schaper2019 - Ratings for 96 words
Last updated 1 months agofrom:45715520c9. Checks:OK: 8 NOTE: 1. Indexed: yes.
Target | Result | Date |
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Doc / Vignettes | OK | Nov 01 2024 |
R-4.5-win-x86_64 | OK | Nov 01 2024 |
R-4.5-linux-x86_64 | OK | Nov 01 2024 |
R-4.4-win-x86_64 | NOTE | Nov 01 2024 |
R-4.4-mac-x86_64 | OK | Nov 01 2024 |
R-4.4-mac-aarch64 | OK | Nov 01 2024 |
R-4.3-win-x86_64 | OK | Nov 01 2024 |
R-4.3-mac-x86_64 | OK | Nov 01 2024 |
R-4.3-mac-aarch64 | OK | Nov 01 2024 |
Exports:anticlusteringbalanced_clusteringbicriterion_anticlusteringcategorical_samplingcategories_to_binarydispersion_objectivediversity_objectivefast_anticlusteringgenerate_exchange_partnersgenerate_partitionskplus_anticlusteringkplus_moment_variablesmatchingmean_sd_tabn_partitionsoptimal_anticlusteringoptimal_dispersionplot_clustersplot_similarityvariance_objectivewce
Some best practices for anticlustering
Rendered fromBest_practices.Rmd
usingknitr::rmarkdown
on Nov 01 2024.Last update: 2024-09-30
Started: 2023-06-29
Speeding up anticlustering
Rendered fromSpeeding_up_anticlustering.Rmd
usingknitr::rmarkdown
on Nov 01 2024.Last update: 2024-04-24
Started: 2023-06-13
Using categorical variables with anticlustering
Rendered fromCategorical_vars.Rmd
usingknitr::rmarkdown
on Nov 01 2024.Last update: 2024-10-01
Started: 2023-06-23
Using the R package anticlust for stimulus selection in experiments
Rendered fromstimulus-selection.Rmd
usingknitr::rmarkdown
on Nov 01 2024.Last update: 2023-10-25
Started: 2019-11-14