Modification of Carmone, Kara & Maxwell Heuristic Identification of Noisy Variables (HINoV) method
Modification of Carmone, Kara & Maxwell Heuristic Identification of Noisy Variables (HINoV) method for symbolic interval data
Descriptive statistics calculated separately for each cluster and variable
Random cluster generation with known structure of clusters
Determination of optimal clustering procedure for a data set
Calculate agreement indices between two partitions
Types of variable (column) and object (row) normalization formulas
Binary data
Interval data
Mixed data
Nominal data
Ordinal data
Metric data with 17 objects and 10 variables (8 stimulant variables, 2 destimulant variables)
Ordinal data with 27 objects and 6 variables (3 stimulant variables, 2 destimulant variables and 1 nominant variable)
Ratio data
Symbolic interval data
Calculates Bray-Curtis distance measure for ratio data
Calculates Generalized Distance Measure
Calculates Sokal-Michener distance measure for nominal variables
Calculates distance between symbolic interval-valued data
Calculates Davies-Bouldin's index
Calculates Calinski-Harabasz pseudo F-statistic
Calculates G2 internal cluster quality index
Calculates G3 internal cluster quality index
Calculates Tibshirani, Walther and Hastie gap index
Calculates Hartigan index
Calculates Krzanowski-Lai index
Calculates Rousseeuw's Silhouette internal cluster quality index
Calculation of initial clusters centers for k-means like alghoritms
Reinforcing measurement scale for ordinal data
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)
Plot categorial data on a scatterplot matrix
Plot categorial data with three-dimensional plots
Plot symbolic interval-valued data on a scatterplot matrix
Modification of replication analysis for cluster validation
Generation of data set containing two clusters with untypical shapes (cube divided into two parts by main diagonal plane)
Generation of data set containing two clusters with untypical ring shapes (circles)
Generation of data set containing three clusters with untypical ring shapes (circles)
Generation of data set containing two clusters with untypical shapes (similar to waxing and waning crescent moon)
Generation of data set containing two clusters with untypical parabolic shapes (worms)
A spectral clustering algorithm