  Identifying meaningful concepts in large data sets can provide valuable
insights into engineering design problems. Concept identification aims at
identifying non-overlapping groups of design instances that are similar in a
joint space of all features, but which are also similar when considering only
subsets of features. These subsets usually comprise features that characterize
a design with respect to one specific context, for example, constructive design
parameters, performance values, or operation modes. It is desirable to evaluate
the quality of design concepts by considering several of these feature subsets
in isolation. In particular, meaningful concepts should not only identify
dense, well separated groups of data instances, but also provide
non-overlapping groups of data that persist when considering pre-defined
feature subsets separately. In this work, we propose to view concept
identification as a special form of clustering algorithm with a broad range of
potential applications beyond engineering design. To illustrate the differences
between concept identification and classical clustering algorithms, we apply a
recently proposed concept identification algorithm to two synthetic data sets
and show the differences in identified solutions. In addition, we introduce the
mutual information measure as a metric to evaluate whether solutions return
consistent clusters across relevant subsets. To support the novel understanding
of concept identification, we consider a simulated data set from a
decision-making problem in the energy management domain and show that the
identified clusters are more interpretable with respect to relevant feature
subsets than clusters found by common clustering algorithms and are thus more
suitable to support a decision maker.
