Learn R Programming

⚠️There's a newer version (0.51-6) of this package.Take me there.

clusterSim (version 0.47-1)

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) , CORMACK, R.M. (1971) , JAJUGA, K., WALESIAK, M., BAK, A. (2003) , CARMONE, F.J., KARA, A., MAXWELL, S. (1999) , DAVIES, D.L., BOULDIN, D.W. (1979) , CALINSKI, T., HARABASZ, J. (1974) , HUBERT, L. (1974) , TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) , KRZANOWSKI, W.J., LAI, Y.T. (1988) , BRECKENRIDGE, J.N. (2000) , WALESIAK, M., DUDEK, A. (2008) ).

Copy Link

Version

Install

install.packages('clusterSim')

Monthly Downloads

5,510

Version

0.47-1

License

GPL (>= 2)

Maintainer

Andrzej Dudek

Last Published

November 30th, 2017

Functions in clusterSim (0.47-1)

comparing.Partitions

Calculate agreement indices between two partitions

cluster.Description

Descriptive statistics calculated separately for each cluster and variable

Random cluster generation with known structure of clusters

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

data.Normalization

Types of variable (column) and object (row) normalization formulas

Determination of optimal clustering procedure for a data set

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

data_patternGDM1

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

data_patternGDM2

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

Calculates Sokal-Michener distance measure for nominal variables

Calculates distance between interval-valued symbolic data

Symbolic interval data

Calculates Hartigan index

Calculates Krzanowski-Lai index

data_symbolic_interval_polish_voivodships

The evaluation of Polish voivodships tourism attractiveness level

Calculates Rousseeuw's Silhouette internal cluster quality index

Calculates Bray-Curtis distance measure for ratio data

Calculates G3 internal cluster quality index

Calculates Generalized Distance Measure

Calculates Davies-Bouldin's index

Calculates Tibshirani, Walther and Hastie gap index

Calculates Calinski-Harabasz pseudo F-statistic

Calculates G2 internal cluster quality index

plotCategorial3d

Plot categorial data with three-dimensional plots

shapes.two.moon

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

initial.Centers

Calculation of initial clusters centers for k-means like alghoritms

replication.Mod

Modification of replication analysis for cluster validation

interval_normalization

Types of normalization formulas for interval-valued symbolic variables

shapes.blocks3d

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

Plot categorial data on a scatterplot matrix

shapes.circles2

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

shapes.circles3

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

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

Plot symbolic interval-valued data on a scatterplot matrix

A spectral clustering algorithm

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

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