Top-down parameter-free clustering of high-dimensional categorical data

A parameter-free, fully-automatic approach to clustering highdimensional categorical data is proposed. The algorithm attempts to improve the overall quality of the whole partition and finds clusters in the data, whose number is naturally established on the basis of the inherent features of the underlying dataset, rather than being previously specified. Experiments on both synthetic and real data prove that the devised algorithm scales linearly and achieves nearly-optimal results in terms of compactness and separation.