All that matters in an MDS map is which point is close to which others. When looking at a map that has non-zero stress, you must keep in mind that the distances among items are imperfect, distorted, representations of the relationships given by your data. The greater the stress, the greater the distortion. In general, however, you can rely on the larger distances as being accurate. This is because the stress function accentuates discrepancies in the larger distances, and the MDS program therefore tries harder to get these right.
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.