  In this work we propose a clustering framework based on the paradigm of
transform learning. In simple terms the representation from transform learning
is used for K-means clustering; however, the problem is not solved in such a
na\"ive piecemeal fashion. The K-means clustering loss is embedded into the
transform learning framework and the joint problem is solved using the
alternating direction method of multipliers. Results on document clustering
show that our proposed approach improves over the state-of-the-art.
