  Mixtures of von Mises-Fisher distributions can be used to cluster data on the
unit hypersphere. This is particularly adapted for high-dimensional directional
data such as texts. We propose in this article to estimate a von Mises mixture
using a l 1 penalized likelihood. This leads to sparse prototypes that improve
clustering interpretability. We introduce an expectation-maximisation (EM)
algorithm for this estimation and explore the trade-off between the sparsity
term and the likelihood one with a path following algorithm. The model's
behaviour is studied on simulated data and, we show the advantages of the
approach on real data benchmark. We also introduce a new data set on financial
reports and exhibit the benefits of our method for exploratory analysis.
