  Information is extracted from large and sparse data sets organized as 3-mode
tensors. Two methods are described, based on best rank-(2,2,2) and rank-(2,2,1)
approximation of the tensor. The first method can be considered as a
generalization of spectral graph partitioning to tensors, and it gives a
reordering of the tensor that clusters the information. The second method gives
an expansion of the tensor in sparse rank-(2,2,1) terms, where the terms
correspond to graphs. The low-rank approximations are computed using an
efficient Krylov-Schur type algorithm that avoids filling in the sparse data.
The methods are applied to topic search in news text, a tensor representing
conference author-terms-years, and network traffic logs.

Numerical Analysis;Numerical Analysis