  Engineering collective adaptive systems (CAS) with learning capabilities is a
challenging task due to their multi-dimensional and complex design space.
Data-driven approaches for CAS design could introduce new insights enabling
system engineers to manage the CAS complexity more cost-effectively at the
design-phase. This paper introduces a systematic approach to reason about
design choices and patterns of learning-based CAS. Using data from a systematic
literature review, reasoning is performed with a novel application of
data-driven methodologies such as clustering, multiple correspondence analysis
and decision trees. The reasoning based on past experience as well as
supporting novel and innovative design choices are demonstrated.
