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anticlust
Subset Partitioning via Anticlustering
View on CRAN: Click
here
Download and install anticlust package within the R console
Install from CRAN:
install.packages("anticlust")
Install from Github:
library("remotes")
install_github("cran/anticlust") Install by package version:
library("remotes")
install_version("anticlust", "0.8.9-1") Attach the package and use:
library("anticlust")
Maintained by
Martin Papenberg
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.anticlust . https://github.com/m-Py/anticlust/issues . https://github.com/m-Py/anticlust . https://m-py.github.io/anticlust/ . anticlust citation info . anticlust results . anticlust.pdf . Some best practices for anticlustering . Using categorical variables with anticlustering . Speeding up anticlustering . Using the R package anticlust for stimulus selection in experiments . anticlust_0.8.10-1.tar.gz . anticlust_0.8.10-1.zip . anticlust_0.8.10-1.zip . anticlust_0.8.10-1.zip . anticlust_0.8.10-1.tgz . anticlust_0.8.10-1.tgz . anticlust_0.8.10-1.tgz . anticlust_0.8.10-1.tgz . anticlust_0.8.10-1.tgz . anticlust_0.8.10-1.tgz . anticlust archive . https://CRAN.R-project.org/package=anticlust .
First Published: 2020-06-29
Latest Update: 2023-10-26
Description:
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 exact and heuristic anticlustering
methods described in Papenberg and Klau (2021;
), Brusco et al. (2020;
), and Papenberg (2023;
). The exact algorithms require that an
integer linear programming solver is installed, either the GNU linear
programming kit ()
together with the interface package 'Rglpk'
(), or the SYMPHONY ILP
solver () together with the
interface package 'Rsymphony'
(). 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 (2023). 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.
How to cite:
Martin Papenberg (2020). anticlust: Subset Partitioning via Anticlustering. R package version 0.8.9-1, https://cran.r-project.org/web/packages/anticlust. Accessed 29 Mar. 2025.
Previous versions and publish date:
0.5.0 (2020-06-29 12:20), 0.5.3 (2020-09-25 11:50), 0.5.6 (2020-11-24 15:40), 0.6.0 (2021-12-01 12:00), 0.6.1 (2021-12-07 09:40), 0.6.3 (2023-01-30 15:30), 0.6.4 (2023-05-02 20:50), 0.7.0 (2023-07-15 23:30), 0.8.0-1 (2023-10-25 15:30), 0.8.0 (2023-09-14 01:00), 0.8.1 (2023-10-26 22:20), 0.8.3 (2024-04-24 12:50), 0.8.5 (2024-05-05 23:20), 0.8.7 (2024-10-01 16:50), 0.8.9-1 (2025-01-20 14:00), 0.8.9 (2025-01-17 16:10), 0.8.10 (2025-03-14 00:20)
Other packages that cited anticlust R package
View anticlust citation profile
Other R packages that anticlust depends,
imports, suggests or enhances
Complete documentation for anticlust
Functions, R codes and Examples using
the anticlust R package
Some associated functions: anticlust . anticlustering . balanced_clustering . bicriterion_anticlustering . categorical_sampling . categories_to_binary . dispersion_objective . diversity_objective . fast_anticlustering . generate_partitions . kplus_anticlustering . kplus_moment_variables . matching . mean_sd_tab . n_partitions . optimal_dispersion . plot_clusters . plot_similarity . schaper2019 . variance_objective . wce .
Some associated R codes: Optimal_Dispersion.R . anticlust.R . bicriterion_iterated_local_search_call.R . c-anticlustering.R . categorical-variable-handling.R . categories_to_binary.R . descriptives_by_cluster.R . exact-anticlustering.R . exact-cluster-editing.R . exchange-method-anticluster-editing.R . exchange-method-generic.R . exchange-method-kmeans-anticlustering.R . generate-partitions.R . ilp-postprocessing.R . ilp-setup.R . ilp-solve.R . input-validation.R . nn-centroid-clustering.R . number-of-partitions.R . objective-dispersion.R . objective-diversity.R . objective-kplus.R . objective-variance.R . plot-similarity.R . plotting.R . repeated-exchange.R . sample-by-category.R . schaper2019.R . util-sort.R . weighted-cluster-editing.R . wrapper-anticlustering.R . wrapper-clustering.R . wrapper-k-plus-anticlustering.R . wrapper-matching.R . Full anticlust package functions and examples
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