  Clustering and prediction are two primary tasks in the fields of unsupervised
and supervised learning, respectively. Although much of the recent advances in
machine learning have been centered around those two tasks, the interdependent,
mutually beneficial relationship between them is rarely explored. One could
reasonably expect appropriately clustering the data would aid the downstream
prediction task and, conversely, a better prediction performance for the
downstream task could potentially inform a more appropriate clustering
strategy. In this work, we focus on the latter part of this mutually beneficial
relationship. To this end, we introduce Deep Goal-Oriented Clustering (DGC), a
probabilistic framework that clusters the data by jointly using supervision via
side-information and unsupervised modeling of the inherent data structure in an
end-to-end fashion. We show the effectiveness of our model on a range of
datasets by achieving prediction accuracies comparable to the state-of-the-art,
while, more importantly in our setting, simultaneously learning congruent
clustering strategies.
