  As data sets continue to grow in size and complexity, effective and efficient
techniques are needed to target important features in the variable space. Many
of the variable selection techniques that are commonly used alongside
clustering algorithms are based upon determining the best variable subspace
according to model fitting in a stepwise manner. These techniques are often
computationally intensive and can require extended periods of time to run; in
fact, some are prohibitively computationally expensive for high-dimensional
data. In this paper, a novel variable selection technique is introduced for use
in clustering and classification analyses that is both intuitive and
computationally efficient. We focus largely on applications in mixture
model-based learning, but the technique could be adapted for use with various
other clustering/classification methods. Our approach is illustrated on both
simulated and real data, highlighted by contrasting its performance with that
of other comparable variable selection techniques on the real data sets.
