Multidimensional Scaling

This chapter discusses other techniques for visualizing high-dimensional data namely, multidimensional scaling (MDS). It also discusses two general methods for solving the MDS problem. The first is called metric multidimensional scaling because it tries to reproduce the original metric or distances. The second method, called non-metric multidimensional scaling (NMMDS), assumes that only the ranks of the distances are known. MDS techniques have proved useful because circumstances often occur where the actual coordinates of the objects are not known, but some type of distance matrix is available. One of the main tasks the researchers and practitioners have is determining the number of dimensions in the MDS model. Proximity measures characterize the similarity or dissimilarity that exists between the objects. The idea of a NMMDS is to demand a less-rigid relationship between the final configuration of the points and the distances.