Multi-Dimensional Scaling is the process of taking snapshots of certain documents from a database in order to point out topical clusters using latent semantic indexing. In comparison, multi dimensional scaling is much more efficient and accurate than singular vector decomposition since a rough approximate of relevance is essential when mixed with other ranking principles.