  Clustering has been a major research topic in the field of machine learning,
one to which Deep Learning has recently been applied with significant success.
However, an aspect of clustering that is not addressed by existing deep
clustering methods, is that of efficiently producing multiple, diverse
partitionings for a given dataset. This is particularly important, as a diverse
set of base clusterings are necessary for consensus clustering, which has been
found to produce better and more robust results than relying on a single
clustering. To address this gap, we propose DivClust, a diversity controlling
loss that can be incorporated into existing deep clustering frameworks to
produce multiple clusterings with the desired degree of diversity. We conduct
experiments with multiple datasets and deep clustering frameworks and show
that: a) our method effectively controls diversity across frameworks and
datasets with very small additional computational cost, b) the sets of
clusterings learned by DivClust include solutions that significantly outperform
single-clustering baselines, and c) using an off-the-shelf consensus clustering
algorithm, DivClust produces consensus clustering solutions that consistently
outperform single-clustering baselines, effectively improving the performance
of the base deep clustering framework.
