SAS Text Miner computes a term-by-document matrix A, where the i-th row and j-th column represents the number of times that the i-th term appears in the j-th document. A has N rows where N is the number of terms in the corpus, and M columns where M is number of documents. You can think of the M columns of A, each of which represents a document, as vectors (or points) in an N-dimensional term-frequency space. The SVD rotates the points so the most variation of your corpus of documents lie in the direction of the first coordinate SVD_1, the second coordinate SVD_2 points in an orthogonal direction that gives the next most variation in the corpus. These vectors represent term-frequency profiles that are convenient. In fact the SVD helps reduce the dimensionality of the problems by only considering a limited number of directions. Since you have 16 SVD vectors, SAS has reduced your corpus to a 16 dimensional subspace of the full term-frequency space.
Clustering is more easily accomplished relative to the SVD coordinate system.
The probability columns represent the likelihood that each document belongs to a cluster. If you have 10 clusters, there should only be 10 probability columns. Each document is assigned to the cluster that corresponds to the maximum likelihood. I am not sure about the details of how the probability is determined. I assume that it is something like a linear discriminant analysis.
