Clusters are groups of items that are closer to each other than to other items. Similarly, the zoo animals like lion, tiger, antelope, monkey, elephant and giraffe form a cluster. When really tight, highly separated clusters occur in perceptual data, it may suggest that each cluster is a domain or subdomain which should be analyzed individually. It is especially important to realize that any relationships observed within such a cluster, such as item a being slightly closer to item b than to c should not be trusted because the exact placement of items within a tight cluster has little effect on overall stress and so may be quite arbitrary. Consequently, it makes sense to extract the submatrix corresponding to a given cluster and re-run the MDS on the submatrix.
Dimensions are item attributes that seem to order the items in the map along a continuum.
For example, an MDS of perceived similarities among breeds of dogs may show a distinct ordering of dogs by size. The ordering might go from right to left, top to bottom, or move diagonally at any angle across the map. At the same time, an independent ordering of dogs according to viciousness might be observed. This ordering might be perpendicular to the size dimension, or it might cut a sharper angle. For example, in the case of similarities among dogs we expect that the reason why two dogs are seen as similar is because they have locations or scores on the identified dimensions.