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clusterSim (version 0.49-2)

Searching for Optimal Clustering Procedure for a Data Set

Description

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) , HUBERT, L., ARABIE, P. (1985) , RAND, W.M. (1971) , JAJUGA, K., WALESIAK, M. (2000) , MILLIGAN, G.W., COOPER, M.C. (1988) , JAJUGA, K., WALESIAK, M., BAK, A. (2003) , DAVIES, D.L., BOULDIN, D.W. (1979) , CALINSKI, T., HARABASZ, J. (1974) , HUBERT, L. (1974) , TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) , BRECKENRIDGE, J.N. (2000) , WALESIAK, M., DUDEK, A. (2008) ).

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Version

Install

install.packages('clusterSim')

Monthly Downloads

2,874

Version

0.49-2

License

GPL (>= 2)

Maintainer

Andrzej Dudek

Last Published

January 6th, 2021

Functions in clusterSim (0.49-2)

data_binary

Binary data
cluster.Gen

Random cluster generation with known structure of clusters
data_mixed

Mixed data
HINoV.Symbolic

Modification of Carmone, Kara \& Maxwell Heuristic Identification of Noisy Variables (HINoV) method for symbolic interval data
cluster.Sim

Determination of optimal clustering procedure for a data set
data_interval

Interval data
HINoV.Mod

Modification of Carmone, Kara \& Maxwell Heuristic Identification of Noisy Variables (HINoV) method
data.Normalization

Types of variable (column) and object (row) normalization formulas
comparing.Partitions

Calculate agreement indices between two partitions
data_nominal

Nominal data
data_patternGDM2

Ordinal data with 27 objects and 6 variables (3 stimulant variables, 2 destimulant variables and 1 nominant variable)
cluster.Description

Descriptive statistics calculated separately for each cluster and variable
index.G2

Calculates G2 internal cluster quality index
index.G1

Calculates Calinski-Harabasz pseudo F-statistic
data_symbolic_interval_polish_voivodships

The evaluation of Polish voivodships tourism attractiveness level
data_symbolic

Symbolic interval data
data_ratio

Ratio data
data_patternGDM1

Metric data with 17 objects and 10 variables (8 stimulant variables, 2 destimulant variables)
index.H

Calculates Hartigan index
index.C

Calculates Hubert & Levin C index - internal cluster quality index
dist.Symbolic

Calculates distance between interval-valued symbolic data
dist.SM

Calculates Sokal-Michener distance measure for nominal variables
index.S

Calculates Rousseeuw's Silhouette internal cluster quality index
plotInterval

Plot symbolic interval-valued data on a scatterplot matrix
initial.Centers

Calculation of initial clusters centers for k-means like alghoritms
plotCategorial3d

Plot categorial data with three-dimensional plots
index.G3

Calculates G3 internal cluster quality index
index.DB

Calculates Davies-Bouldin's index
shapes.two.moon

Generation of data set containing two clusters with untypical shapes (similar to waxing and waning crescent moon)
index.Gap

Calculates Tibshirani, Walther and Hastie gap index
pattern.GDM2

An application of GDM2 distance for ordinal data to compute the distances of objects from the pattern object (upper or lower)
interval_normalization

Types of normalization formulas for interval-valued symbolic variables
dist.BC

Calculates Bray-Curtis distance measure for ratio data
plotCategorial

Plot categorial data on a scatterplot matrix
pattern.GDM1

An application of GDM1 distance for metric data to compute the distances of objects from the pattern object (upper or lower)
replication.Mod

Modification of replication analysis for cluster validation
index.KL

Calculates Krzanowski-Lai index
shapes.blocks3d

Generation of data set containing two clusters with untypical shapes (cube divided into two parts by main diagonal plane)
speccl

A spectral clustering algorithm
shapes.circles2

Generation of data set containing two clusters with untypical ring shapes (circles)
dist.GDM

Calculates Generalized Distance Measure
shapes.circles3

Generation of data set containing three clusters with untypical ring shapes (circles)
shapes.worms

Generation of data set containing two clusters with untypical parabolic shapes (worms)