  We present an experimental study of the preferential concentration of
sub-Kolmogorov inertial particles in active-grid-generated homogeneous and
isotropic turbulence, characterized via Vorono\"i tessellations.
  We show that the detection and quantification of clusters and voids is
influenced by the intensity of the laser and high values of particles volume
fraction $\phi_v$. Different biases on the statistics of Vorono\"i cells are
analyzed to improve the reliability of the detection and the robustness in the
characterization of clusters and voids. We do this by adapting Big-Data
techniques that allow to process the particle images up to 10 times faster than
standard algorithms.
  Finally, as preferential concentration is known to depend on multiple
parameters, we performed experiments where one parameter was varied and all
others were kept constant ($\phi_v$, Reynolds number based on the Taylor length
scale $Re_\lambda$, and residence time of the particles interacting with the
turbulence). Our results confirm, in agreement with published work, that
clustering increases with both $\phi_v$ and $Re_\lambda$. On the other hand, we
find new evidence that the mean size of clusters increases with $Re_\lambda$
but decreases with $\phi_v$ and that the cluster settling velocity is strongly
affected by $Re_\lambda$ up to the maximum value studied here, $Re_\lambda =
250$.
