  Subgroup discovery is a descriptive and exploratory data mining technique to
identify subgroups in a population that exhibit interesting behavior with
respect to a variable of interest. Subgroup discovery has numerous applications
in knowledge discovery and hypothesis generation, yet it remains inapplicable
for unstructured, high-dimensional data such as images. This is because
subgroup discovery algorithms rely on defining descriptive rules based on
(attribute, value) pairs, however, in unstructured data, an attribute is not
well defined. Even in cases where the notion of attribute intuitively exists in
the data, such as a pixel in an image, due to the high dimensionality of the
data, these attributes are not informative enough to be used in a rule. In this
paper, we introduce the subgroup-aware variational autoencoder, a novel
variational autoencoder that learns a representation of unstructured data which
leads to subgroups with higher quality. Our experimental results demonstrate
the effectiveness of the method at learning subgroups with high quality while
supporting the interpretability of the concepts.
