Type: Package
Package: anticlust
Title: Subset Partitioning via Anticlustering
Version: 0.5.3
Authors@R: 
    c(person(given = "Martin",
             family = "Papenberg",
             role = c("aut", "cre"),
             email = "martin.papenberg@hhu.de",
             comment = c(ORCID = "0000-0002-9900-4268")),
      person(given = "Meik",
             family = "Michalke",
             role = "ctb",
             comment = "centroid based clustering algorithm"),
      person(given = c("Gunnar", "W."),
             family = "Klau",
             role = "ths"),
      person(given = c("Juliane", "V."),
             family = "Tkotz",
             role = "ctb",
             comment = "package logo"))
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)
Maintainer: Martin Papenberg <martin.papenberg@hhu.de>
Description: The method of anticlustering partitions a pool of
    elements into groups (i.e., anticlusters) in such a way that the
    between-group similarity is maximized and -- at the same time -- the
    within-group heterogeneity is maximized. This 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
    (2020; <doi:10.1037/met0000301>). The exact approach requires 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. 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: Rglpk, testthat, knitr, rmarkdown
VignetteBuilder: knitr, rmarkdown
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
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.
NeedsCompilation: no
Packaged: 2020-09-25 09:23:54 UTC; martin
Repository: CRAN
Date/Publication: 2020-09-25 09:50:05 UTC
