  Effective clustering of biomedical data is crucial in precision medicine,
enabling accurate stratifiction of patients or samples. However, the growth in
availability of high-dimensional categorical data, including `omics data,
necessitates computationally efficient clustering algorithms. We present
VICatMix, a variational Bayesian finite mixture model designed for the
clustering of categorical data. The use of variational inference (VI) in its
training allows the model to outperform competitors in term of efficiency,
while maintaining high accuracy. VICatMix furthermore performs variable
selection, enhancing its performance on high-dimensional, noisy data. The
proposed model incorporates summarisation and model averaging to mitigate poor
local optima in VI, allowing for improved estimation of the true number of
clusters simultaneously with feature saliency. We demonstrate the performance
of VICatMix with both simulated and real-world data, including applications to
datasets from The Cancer Genome Atlas (TCGA), showing its use in cancer
subtyping and driver gene discovery. We demonstrate VICatMix's utility in
integrative cluster analysis with different `omics datasets, enabling the
discovery of novel subtypes.
  \textbf{Availability:} VICatMix is freely available as an R package,
incorporating C++ for faster computation, at
\url{https://github.com/j-ackierao/VICatMix}.
