  Dirichlet process mixture (DPM) models tend to produce many small clusters
regardless of whether they are needed to accurately characterize the data -
this is particularly true for large data sets. However, interpretability,
parsimony, data storage and communication costs all are hampered by having
overly many clusters. We propose a powered Chinese restaurant process to limit
this kind of problem and penalize over clustering. The method is illustrated
using some simulation examples and data with large and small sample size
including MNIST and the Old Faithful Geyser data.
