  Clustering is a fundamental machine learning task which has been widely
studied in the literature. Classic clustering methods follow the assumption
that data are represented as features in a vectorized form through various
representation learning techniques. As the data become increasingly complicated
and complex, the shallow (traditional) clustering methods can no longer handle
the high-dimensional data type. With the huge success of deep learning,
especially the deep unsupervised learning, many representation learning
techniques with deep architectures have been proposed in the past decade.
Recently, the concept of Deep Clustering, i.e., jointly optimizing the
representation learning and clustering, has been proposed and hence attracted
growing attention in the community. Motivated by the tremendous success of deep
learning in clustering, one of the most fundamental machine learning tasks, and
the large number of recent advances in this direction, in this paper we conduct
a comprehensive survey on deep clustering by proposing a new taxonomy of
different state-of-the-art approaches. We summarize the essential components of
deep clustering and categorize existing methods by the ways they design
interactions between deep representation learning and clustering. Moreover,
this survey also provides the popular benchmark datasets, evaluation metrics
and open-source implementations to clearly illustrate various experimental
settings. Last but not least, we discuss the practical applications of deep
clustering and suggest challenging topics deserving further investigations as
future directions.

Machine Learning;Artificial Intelligence