  Deep clustering has exhibited remarkable performance; however, the
over-confidence problem, i.e., the estimated confidence for a sample belonging
to a particular cluster greatly exceeds its actual prediction accuracy, has
been overlooked in prior research. To tackle this critical issue, we pioneer
the development of a calibrated deep clustering framework. Specifically, we
propose a novel dual-head (calibration head and clustering head) deep
clustering model that can effectively calibrate the estimated confidence and
the actual accuracy. The calibration head adjusts the overconfident predictions
of the clustering head, generating prediction confidence that match the model
learning status. Then, the clustering head dynamically select reliable
high-confidence samples estimated by the calibration head for pseudo-label
self-training. Additionally, we introduce an effective network initialization
strategy that enhances both training speed and network robustness. The
effectiveness of the proposed calibration approach and initialization strategy
are both endorsed with solid theoretical guarantees. Extensive experiments
demonstrate the proposed calibrated deep clustering model not only surpasses
state-of-the-art deep clustering methods by 10 times in terms of expected
calibration error but also significantly outperforms them in terms of
clustering accuracy.
