Title: GALILEO: A Generalized Low-Entropy Mixture Model,
Abstract: We present a new method of generating mixture models for data with
categorical attributes. The keys to this approach are an entropy-based density
metric in categorical space and annealing of high-entropy/low-density
components from an initial state with many components. Pruning of low-density
components using the entropy-based density allows GALILEO to consistently find
high-quality clusters and the same optimal number of clusters. GALILEO has
shown promising results on a range of test datasets commonly used for
categorical clustering benchmarks. We demonstrate that the scaling of GALILEO
is linear in the number of records in the dataset, making this method suitable
for very large categorical datasets.