Alongside the profusion of clustering methods for observations, there are few specific methods for clustering variables. This presentation will provide an overview of the methods that can be used: partitioning, hierarchical methods and mixture models, with a particular focus on the case of sets combining quantitative and qualitative variables. The use of p-values to eliminate the effects due to different numbers of categories is often recommended, but when the number of units is very large, all p-values are close to zero (paradox of large samples). We propose to replace them by z-scores. These different approaches will be compared on real data sets.