High-dimensional data may often contain both numerical and categorical features, and in some cases features may be available as natural groups (repeated measurements, categories of features, ...). Clustering this kind of data raises several issues: how to simultaneously deal with numerical and categorical features? how to build meaningful clusters of the input entities? how to select the most informative features or groups of features for the clustering? In the k-means framework, one may rely on a penalised version of the between-cluster variance, and find both the best partitioning of the data, and the most informative features or groups of features. The present manuscript illustrates sparse k-means and group sparse k-means for mixed data, using the vimpclust package. The example provided on a small real-life dataset shows how feature selection may be directly combined with clustering, and provide a meaningful selection while preserving the quality of the clustering.