Searching for optimal clustering procedure for a data set

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Documentation for package `clusterSim' version 0.33-1

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cluster.Description Descriptive statistics calculated separately for each cluster and variable
cluster.Gen Random cluster generation with known structure of clusters
cluster.Sim Determination of optimal clustering procedure for a data set
data.Normalization Types of variable normalization formulas
data_binary Binary data
data_interval Interval data
data_mixed Mixed data
data_nominal Nominal data
data_ordinal Ordinal data
data_ratio Ratio data
data_symbolic Symbolic interval data
dist.BC Calculates Bray-Curtis distance measure for ratio data
dist.GDM Calculates Generalized Distance Measure
dist.SM Calculates Sokal-Michener distance measure for nominal variables
dist.Symbolic Calculates distance between symbolic interval-valued data
GDM Calculates Generalized Distance Measure
GDM1 Calculates Generalized Distance Measure
GDM2 Calculates Generalized Distance Measure
HINoV.Mod Modification of Carmone, Kara & Maxwell Heuristic Identification of Noisy Variables (HINoV) method
HINoV.Symbolic Modification of Carmone, Kara & Maxwell Heuristic Identification of Noisy Variables (HINoV) method for symbolic interval data
index.G1 Calculates Calinski-Harabasz pseudo F-statistic
index.G2 Calculates G2 internal cluster quality index
index.G3 Calculates G3 internal cluster quality index
index.Gap Calculates Tibshirani, Walther and Hastie gap index
index.H Calculates Hartigan index
index.KL Calculates Krzanowski-Lai index
index.S Calculates Rousseeuw's Silhouette internal cluster quality index
initial.Centers Calculation of initial clusters centers for k-means like alghoritms
plotCategorial Plot categorial data on a scatterplot matrix
plotCategorial3d Plot categorial data with three-dimensional plots
plotInterval Plot symbolic interval-valued data on a scatterplot matrix
replication.Mod Modification of replication analysis for cluster validation