Approaches range from classical line‐simplification algorithms to advanced semantic compression methods that incorporate clustering and multi-resolution analysis.

We introduce a novel statistical procedure for clustering categorical data based on Hamming distance (HD) vectors. The proposed method is conceptually simple and computationally straightforward, ...

We develop a flexible model-based procedure for clustering functional data. The technique can be applied to all types of curve data but is particularly useful when individuals are observed at a sparse ...