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

Spectral clustering is quite complex, but it can reveal patterns in data that aren't revealed by other clustering techniques.

A k-means-type algorithm is proposed for efficiently clustering data constrained to lie on the surface of a p-dimensional unit sphere, or data that are mean-zero-unit-variance standardized ...

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, ...

Virtualization and clustering can be two faces of the same coin. Computing virtualization is a very hot topic for data center managers. Whether the motivation is higher utilization, reduced ...