  Combining machine clustering with deep models has shown remarkable
superiority in deep clustering. It modifies the data processing pipeline into
two alternating phases: feature clustering and model training. However, such
alternating schedule may lead to instability and computational burden issues.
We propose a centerless clustering algorithm called Probability Aggregation
Clustering (PAC) to proactively adapt deep learning technologies, enabling easy
deployment in online deep clustering. PAC circumvents the cluster center and
aligns the probability space and distribution space by formulating clustering
as an optimization problem with a novel objective function. Based on the
computation mechanism of the PAC, we propose a general online probability
aggregation module to perform stable and flexible feature clustering over
mini-batch data and further construct a deep visual clustering framework deep
PAC (DPAC). Extensive experiments demonstrate that PAC has superior clustering
robustness and performance and DPAC remarkably outperforms the state-of-the-art
deep clustering methods.
