  Regression uses supervised machine learning to find a model that combines
several independent variables to predict a dependent variable based on ground
truth (labeled) data, i.e., tuples of independent and dependent variables
(labels). Similarly, aggregation also combines several independent variables to
a dependent variable. The dependent variable should preserve properties of the
independent variables, e.g., the ranking or relative distance of the
independent variable tuples, and/or represent a latent ground truth that is a
function of these independent variables. However, ground truth data is not
available for finding the aggregation model. Consequently, aggregation models
are data agnostic or can only be derived with unsupervised machine learning
approaches.
  We introduce a novel unsupervised aggregation approach based on intrinsic
properties of unlabeled training data, such as the cumulative probability
distributions of the single independent variables and their mutual
dependencies.
  We present an empirical evaluation framework that allows assessing the
proposed approach against other aggregation approaches from two perspectives:
(i) how well the aggregation output represents properties of the input tuples,
and (ii) how well can aggregated output predict a latent ground truth. To this
end, we use data sets for assessing supervised regression approaches that
contain explicit ground truth labels. However, the ground truth is not used for
deriving the aggregation models, but it allows for the assessment from a
perspective (ii). More specifically, we use regression data sets from the UCI
machine learning repository and benchmark several data-agnostic and
unsupervised approaches for aggregation against ours.
  The benchmark results indicate that our approach outperforms the other
data-agnostic and unsupervised aggregation approaches. It is almost on par with
linear regression.
