  Identification of coherent structures is an essential step to describe and
model turbulence generation mechanisms in wall-bounded flows. To this end we
present a clustering method based on Latent Dirichlet Allocation (LDA), a
generative probabilistic model for collections of discrete data. The method is
applied for structure identification to the instantaneous Reynolds stress in
turbulent channel flow at moderate Reynolds number R {\tau} = 590. LDA computes
a robust flow description in terms of a hierarchy of vertically connected
structure fragments, the characteristics of which scale with the wall distance,
in agreement with the wall-attached eddy hypothesis of Townsend (1961). We show
that the model can be used for field reconstruction with a complexity that can
be compared with that of Proper Orthogonal Decomposition (POD). It can also be
used to generate synthetic fields, the statistics of which mimic those of the
original database. These findings highlight the potential of LDA for data
analysis, compression and generation.
