  Facilitated by the powerful feature extraction ability of neural networks,
deep clustering has achieved great success in analyzing high-dimensional and
complex real-world data. The performance of deep clustering methods is affected
by various factors such as network structures and learning objectives. However,
as pointed out in this survey, the essence of deep clustering lies in the
incorporation and utilization of prior knowledge, which is largely ignored by
existing works. From pioneering deep clustering methods based on data structure
assumptions to recent contrastive clustering methods based on data augmentation
invariances, the development of deep clustering intrinsically corresponds to
the evolution of prior knowledge. In this survey, we provide a comprehensive
review of deep clustering methods by categorizing them into six types of prior
knowledge. We find that in general the prior innovation follows two trends,
namely, i) from mining to constructing, and ii) from internal to external.
Besides, we provide a benchmark on five widely-used datasets and analyze the
performance of methods with diverse priors. By providing a novel prior
knowledge perspective, we hope this survey could provide some novel insights
and inspire future research in the deep clustering community.
