| acore | Extraction of alpha cores for soft clusters | 
| cselection | Repeated soft clustering for detection of empty clusters for estimation of optimised number of clusters | 
| Dmin | Calculation of minimum centroid distance for a range of cluster numbers for estimation of optimised number of clusters | 
| fill.NA | Replacement of missing values | 
| filter.NA | Filtering of genes based on number of non-available expression values. | 
| filter.std | Filtering of genes based on their standard deviation. | 
| kmeans2 | K-means clustering for gene expression data | 
| kmeans2.plot | Plotting results for k-means clustering | 
| membership | Calculating of membership values for new data based on existing clustering | 
| mestimate | Estimate for optimal fuzzifier m | 
| mfuzz | Function for soft clustering based on fuzzy c-means. | 
| mfuzz.plot | Plotting results for soft clustering | 
| mfuzz.plot2 | Plotting results for soft clustering with additional options | 
| mfuzzColorBar | Plots a colour bar | 
| Mfuzzgui | Graphical user interface for Mfuzz package | 
| overlap | Calculation of the overlap of soft clusters | 
| overlap.plot | Visualisation of cluster overlap and global clustering structure | 
| partcoef | Calculation of the partition coefficient matrix for soft clustering | 
| randomise | Randomisation of data | 
| standardise | Standardization of expression data for clustering. | 
| standardise2 | Standardization in regards to selected time-point | 
| table2eset | Conversion of table to Expression set object. | 
| top.count | Determines the number for which each gene has highest membership value in all cluster | 
| yeast | Gene expression data of the yeast cell cycle | 
| yeast.table | Gene expression data of the yeast cell cycle as table | 
| yeast.table2 | Gene expression data of the yeast cell cycle as table |