Type: Package
Package: anticlust
Title: Subset Partitioning via Anticlustering
Version: 0.6.1
Authors@R: c(
person("Martin", "Papenberg", , "martin.papenberg@hhu.de", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-9900-4268")),
person("Meik", "Michalke", role = "ctb",
comment = "centroid based clustering algorithm"),
person(c("Gunnar", "W."), "Klau", role = "ths"),
person(c("Juliane", "V."), "Tkotz", role = "ctb",
comment = "package logo"),
person("Martin", "Breuer", role = "ctb",
comment = "Bicriterion algorithm by Brusco et al.")
)
Author: Martin Papenberg [aut, cre] (<https://orcid.org/0000-0002-9900-4268>),
Meik Michalke [ctb] (centroid based clustering algorithm),
Gunnar W. Klau [ths],
Juliane V. Tkotz [ctb] (package logo),
Martin Breuer [ctb] (Bicriterion algorithm by Brusco et al.)
Maintainer: Martin Papenberg <martin.papenberg@hhu.de>
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
low similarity of the different 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 function anticlustering() implements exact and
heuristic anticlustering algorithms as described in Papenberg and Klau
(2021; <doi:10.1037/met0000301>). The exact algorithms require that
the GNU linear programming kit
(<https://www.gnu.org/software/glpk/glpk.html>) is available and the R
package 'Rglpk' (<https://cran.R-project.org/package=Rglpk>) is
installed. A bicriterion anticlustering method proposed by Brusco et al.
(2020; <doi:10.1111/bmsp.12186>) is available through the function
bicriterion_anticlustering(). 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.
License: MIT + file LICENSE
URL: https://github.com/m-Py/anticlust
BugReports: https://github.com/m-Py/anticlust/issues
Depends: R (>= 3.6.0)
Imports: Matrix, RANN (>= 2.6.0)
Suggests: knitr, Rglpk, rmarkdown, testthat
VignetteBuilder: knitr, rmarkdown
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
RoxygenNote: 7.1.2
SystemRequirements: The exact (anti)clustering algorithms require that
the GNU linear programming kit (GLPK library) is installed
(<http://www.gnu.org/software/glpk/>). Rendering the vignette
requires pandoc.
Packaged: 2021-12-06 14:56:57 UTC; martin
Repository: CRAN
Date/Publication: 2021-12-07 08:40:02 UTC