Package: clusterSim 0.51-4

Andrzej Dudek

clusterSim:Searching for Optimal Clustering Procedure for a Data Set

Distance measures (GDM1, GDM2, Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap, Davies-Bouldin), data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method, replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data). (MILLIGAN, G.W., COOPER, M.C. (1985) <doi:10.1007/BF02294245>, HUBERT, L., ARABIE, P. (1985) <doi:10.1007%2FBF01908075>, RAND, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, JAJUGA, K., WALESIAK, M. (2000) <doi:10.1007/978-3-642-57280-7_11>, MILLIGAN, G.W., COOPER, M.C. (1988) <doi:10.1007/BF01897163>, JAJUGA, K., WALESIAK, M., BAK, A. (2003) <doi:10.1007/978-3-642-55721-7_12>, DAVIES, D.L., BOULDIN, D.W. (1979) <doi:10.1109/TPAMI.1979.4766909>, CALINSKI, T., HARABASZ, J. (1974) <doi:10.1080/03610927408827101>, HUBERT, L. (1974) <doi:10.1080/01621459.1974.10480191>, TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) <doi:10.1111/1467-9868.00293>, BRECKENRIDGE, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, WALESIAK, M., DUDEK, A. (2008) <doi:10.1007/978-3-540-78246-9_11>).

Authors:Marek Walesiak [aut], Andrzej Dudek [aut, cre]

clusterSim_0.51-4.tar.gz
clusterSim_0.51-4.tar.gz(r-4.5-noble)clusterSim_0.51-4.tar.gz(r-4.4-noble)
clusterSim_0.51-4.tgz(r-4.4-emscripten)clusterSim_0.51-4.tgz(r-4.3-emscripten)
clusterSim.pdf |clusterSim.html✨
clusterSim/json (API)

# InstallclusterSim in R:
install.packages('clusterSim',repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

38 exports 2 stars 2.46 score 11 dependencies 11 dependents 5.2k downloads

Last updated 9 days agofrom:00d1addbd6

Exports:cluster.Descriptioncluster.Gencluster.Simcomparing.Partitionsdata.Normalizationdist.BCdist.GDMdist.SMdist.SymbolicGDMGDM1GDM2HINoV.ModHINoV.Symbolicindex.Cindex.DBindex.G1index.G2index.G3index.Gapindex.Hindex.KLindex.Sinitial.Centersinterval_normalizationordinalToMetricpattern.GDM1pattern.GDM2plotCategorialplotIntervalreplication.Modshapes.blocks3dshapes.bulls.eyeshapes.circles2shapes.circles3shapes.two.moonshapes.wormsspeccl

Dependencies:ade4classclustere1071latticeMASSpixmapproxyRcppRcppArmadillosp

Readme and manuals

Help Manual

Help pageTopics
Descriptive statistics calculated separately for each cluster and variablecluster.Description
Random cluster generation with known structure of clusterscluster.Gen
Determination of optimal clustering procedure for a data setcluster.Sim
Calculate agreement indices between two partitionscomparing.Partitions
Binary datadata_binary
Interval datadata_interval
Mixed datadata_mixed
Nominal datadata_nominal
Ordinal datadata_ordinal
Metric data with 17 objects and 10 variables (8 stimulant variables, 2 destimulant variables)data_patternGDM1
Ordinal data with 27 objects and 6 variables (3 stimulant variables, 2 destimulant variables and 1 nominant variable)data_patternGDM2
Ratio datadata_ratio
Symbolic interval datadata_symbolic
The evaluation of Polish voivodships tourism attractiveness leveldata_symbolic_interval_polish_voivodships
Types of variable (column) and object (row) normalization formulasdata.Normalization
Calculates Bray-Curtis distance measure for ratio datadist.BC
Calculates Generalized Distance Measuredist.GDM GDM GDM1 GDM2
Calculates Sokal-Michener distance measure for nominal variablesdist.SM
Calculates distance between interval-valued symbolic datadist.Symbolic
Modification of Carmone, Kara & Maxwell Heuristic Identification of Noisy Variables (HINoV) methodHINoV.Mod
Modification of Carmone, Kara & Maxwell Heuristic Identification of Noisy Variables (HINoV) method for symbolic interval dataHINoV.Symbolic
Calculates Hubert & Levin C index - internal cluster quality indexindex.C
Calculates Davies-Bouldin's indexindex.DB
Calculates Calinski-Harabasz pseudo F-statisticindex.G1
Calculates G2 internal cluster quality indexindex.G2
Calculates G3 internal cluster quality indexindex.G3
Calculates Tibshirani, Walther and Hastie gap indexindex.Gap
Calculates Hartigan indexindex.H
Calculates Krzanowski-Lai indexindex.KL
Calculates Rousseeuw's Silhouette internal cluster quality indexindex.S
Calculation of initial clusters centers for k-means like alghoritmsinitial.Centers
Types of normalization formulas for interval-valued symbolic variablesinterval_normalization
Reinforcing measurement scale for ordinal dataordinalToMetric
An application of GDM1 distance for metric data to compute the distances of objects from the pattern object (upper or lower)pattern.GDM1
An application of GDM2 distance for ordinal data to compute the distances of objects from the pattern object (upper or lower)pattern.GDM2
Plot categorial data on a scatterplot matrixplotCategorial
Plot symbolic interval-valued data on a scatterplot matrixplotInterval
Modification of replication analysis for cluster validationreplication.Mod
Generation of data set containing two clusters with untypical shapes (cube divided into two parts by main diagonal plane)shapes.blocks3d
Generation of data set containing two clusters with untypical ring shapes (circles)shapes.bulls.eye shapes.circles2
Generation of data set containing three clusters with untypical ring shapes (circles)shapes.circles3
Generation of data set containing two clusters with untypical shapes (similar to waxing and waning crescent moon)shapes.two.moon
Generation of data set containing two clusters with untypical parabolic shapes (worms)shapes.worms
A spectral clustering algorithmspeccl