FCPS: Fundamental Clustering Problems Suite
Over sixty clustering algorithms are provided in this package with consistent input and output, which enables the user to try out algorithms swiftly. Additionally, 26 statistical approaches for the estimation of the number of clusters as well as the mirrored density plot (MD-plot) of clusterability are implemented. The packages is published in Thrun, M.C., Stier Q.: "Fundamental Clustering Algorithms Suite" (2021), SoftwareX, <doi:10.1016/j.softx.2020.100642>. Moreover, the fundamental clustering problems suite (FCPS) offers a variety of clustering challenges any algorithm should handle when facing real world data, see Thrun, M.C., Ultsch A.: "Clustering Benchmark Datasets Exploiting the Fundamental Clustering Problems" (2020), Data in Brief, <doi:10.1016/j.dib.2020.105501>.
Version:
1.3.5
Depends:
R (≥ 3.5.0)
Suggests:
mlpack,
kernlab,
cclust,
dbscan,
kohonen,
MCL,
ADPclust,
cluster,
DatabionicSwarm,
orclus,
flexclust,
ABCanalysis,
apcluster,
pracma,
EMCluster,
pdfCluster,
parallelDist,
plotly,
ProjectionBasedClustering,
GeneralizedUmatrix,
mstknnclust,
densityClust, parallel,
energy,
R.utils,
tclust,
Spectrum,
genie,
protoclust,
fastcluster,
clusterability,
signal,
reshape2,
PPCI,
clustrd,
smacof,
rgl,
prclust,
CEC,
dendextend,
moments,
prabclus,
VarSelLCM,
sparcl,
mixtools,
HDclassif,
clustvarsel,
yardstick,
knitr,
rmarkdown,
igraph,
leiden,
clustMixType,
clusterSim,
NetworkToolbox,
ClusterR,
partitionComparison,
aricode
Published:
2025年10月30日
Author:
Michael Thrun
ORCID iD
[aut, cre, cph],
Peter Nahrgang [ctr, ctb],
Felix Pape [ctr, ctb],
Vasyl Pihur [ctb],
Guy Brock [ctb],
Susmita Datta [ctb],
Somnath Datta [ctb],
Luis Winckelmann [com],
Alfred Ultsch [dtc, ctb],
Quirin Stier [ctb, rev]
Maintainer:
Michael Thrun <m.thrun at gmx.net>
NeedsCompilation:
no
SystemRequirements:
Pandoc (>= 1.12.3)
Documentation:
Downloads:
Reverse dependencies:
Linking:
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