| 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 |
| comparing.Partitions | Calculate agreement indices between two partitions |
| data.Normalization | Types of variable (column) and object (row) normalization formulas |
| data_binary | Binary data |
| data_interval | Interval data |
| data_mixed | Mixed data |
| data_nominal | Nominal data |
| data_ordinal | Ordinal 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) |
| data_ratio | Ratio data |
| data_symbolic | Symbolic interval data |
| data_symbolic_interval_polish_voivodships | The evaluation of Polish voivodships tourism attractiveness level |
| 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 interval-valued symbolic 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.C | Calculates Hubert & Levin C index - internal cluster quality index |
| index.DB | Calculates Davies-Bouldin's index |
| 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 |
| interval_normalization | Types of normalization formulas for interval-valued symbolic variables |
| ordinalToMetric | Reinforcing measurement scale for ordinal data |
| pattern.GDM1 | An application of GDM1 distance for metric data to compute the distances of objects from the pattern object (upper or lower) |
| pattern.GDM2 | An application of GDM2 distance for ordinal data to compute the distances of objects from the pattern object (upper or lower) |
| plotCategorial | Plot categorial data on a scatterplot matrix |
| plotInterval | Plot symbolic interval-valued data on a scatterplot matrix |
| replication.Mod | Modification of replication analysis for cluster validation |
| shapes.blocks3d | Generation of data set containing two clusters with untypical shapes (cube divided into two parts by main diagonal plane) |
| shapes.bulls.eye | Generation of data set containing two clusters with untypical ring shapes (circles) |
| 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) |
| shapes.two.moon | Generation of data set containing two clusters with untypical shapes (similar to waxing and waning crescent moon) |
| shapes.worms | Generation of data set containing two clusters with untypical parabolic shapes (worms) |
| speccl | A spectral clustering algorithm |