"Rich Ulrich" <[EMAIL PROTECTED]> wrote: > Has somebody cared enough about MDS to update the > computer programs? It's long been my impression that > 'marketing' was using MDS. From google, it also seems > like MDS sometimes is included in the tools of data mining.
MDS has become slightly outdated, but there has been some good work into the same direction recently, primarily in the direction of locally linear embedding. The problem with MDS is globality: in most stress functions short dissimilarities are approximated with a similar precision as large dissimilarities. In reality, what a human analyst expects from MDS is more a structure alike clustering: one that would identify groups of similar objects. The 'old' solution was Shepard's non-metric MDS. NMDS attempts to modify the dissimilarity matrix so that the distance rankings are maintained, rather than metric deviations. For example, we are not interested in the exact distances between A, B and C, but we do want distance A-B to be greater than A-C if C is more dissimilar from A than is B. It does not work well in practice, unfortunately. Today, new methods have emerged. For example, locally linear embedding instead only evaluates the distances from an object to K of its nearest neighbors. This yields nice results. Good starting points for further exploration are http://www.cs.toronto.edu/~roweis/lle/ http://basis.stanford.edu/carrie-web/ Aleks . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
