  We propose Bayesian model averaging (BMA) as a method for postprocessing the
results of model-based clustering. Given a number of competing models,
appropriate model summaries are averaged, using the posterior model
probabilities, instead of being taken from a single "best" model. We
demonstrate the use of BMA in model-based clustering for a number of datasets.
We show that BMA provides a useful summary of the clustering of observations
while taking model uncertainty into account. Further, we show that BMA in
conjunction with model-based clustering gives a competitive method for density
estimation in a multivariate setting. Applying BMA in the model-based context
is fast and can give enhanced modeling performance.
