  We present a framework for clustering with cluster-specific feature
selection. The framework, CRAFT, is derived from asymptotic log posterior
formulations of nonparametric MAP-based clustering models. CRAFT handles
assorted data, i.e., both numeric and categorical data, and the underlying
objective functions are intuitively appealing. The resulting algorithm is
simple to implement and scales nicely, requires minimal parameter tuning,
obviates the need to specify the number of clusters a priori, and compares
favorably with other methods on real datasets.

Machine Learning;Machine Learning