  Clustering is central to many data-driven application domains and has been
studied extensively in terms of distance functions and grouping algorithms.
Relatively little work has focused on learning representations for clustering.
In this paper, we propose Deep Embedded Clustering (DEC), a method that
simultaneously learns feature representations and cluster assignments using
deep neural networks. DEC learns a mapping from the data space to a
lower-dimensional feature space in which it iteratively optimizes a clustering
objective. Our experimental evaluations on image and text corpora show
significant improvement over state-of-the-art methods.
