Gaussian mixture models provide a probabilistically sound clustering approach.
However, their tendency to be over-parameterized endangers their utility
in high dimensions. To induce sparsity, penalized model-based clustering strategies
have been explored. Some of these approaches, exploiting the link between
Gaussian graphical models and mixtures, allow to handle large precision matrices,
encoding variables relationships. By assuming similar components sparsity levels,
these methods fall short when the dependence structures are group-dependent. Our
proposal, by penalizing group-specific transformations of the precision matrices, automatically
handles situations where under or over-connectivity between variables
is witnessed. The performances of the method are shown via a real data experimen
