  With the recent growth in data availability and complexity, and the
associated outburst of elaborate modelling approaches, model selection tools
have become a lifeline, providing objective criteria to deal with this
increasingly challenging landscape. In fact, basing predictions and inference
on a single model may be limiting if not harmful; ensemble approaches, which
combine different models, have been proposed to overcome the selection step,
and proven fruitful especially in the supervised learning framework.
Conversely, these approaches have been scantily explored in the unsupervised
setting. In this work we focus on the model-based clustering formulation, where
a plethora of mixture models, with different number of components and
parametrizations, is typically estimated. We propose an ensemble clustering
approach that circumvents the single best model paradigm, while improving
stability and robustness of the partitions. A new density estimator, being a
convex linear combination of the density estimates in the ensemble, is
introduced and exploited for group assignment. As opposed to the standard case,
where clusters are typically associated to the components of the selected
mixture model, we define partitions by borrowing the modal, or nonparametric,
formulation of the clustering problem, where groups are linked with
high-density regions. Staying in the density-based realm we thus show how
blending together parametric and nonparametric approaches may be beneficial
from a clustering perspective.
