We explored the use of multidimensional scaling (MDS) of tree-to-tree pairwise distances to visualize the relationships among sets of phylogenetic trees. We found the technique to be useful for exploring "tree islands" (sets of topologically related trees among larger sets of near-optimal trees), for comparing sets of trees obtained from bootstrapping and Bayesian sampling, for comparing trees ...

Multidimensional scaling (MDS) techniques provide a promising measurement strategy for characterizing individual differences in cognitive processing, which many clinical theories associate with the development, maintenance, and treatment of psychopathology. The authors describe the use of deterministic and probabilistic MDS techniques for investigating numerous aspects of perceptual organizatio...

Multidimensional Scaling (MDS) is a powerful dimension reduction technique for embedding high-dimensional data into a lowdimensional target space. Thereby, the distance relationships in the source are reconstructed in the target space as best as possible according to a given embedding criterion. Here, a new stress function with intuitive properties and a very good convergence behavior is presen...

Multidimensional scaling (MDS) is a collection of data analytic techniques for constructing conngura-tions of points from dissimilarity information about interpoint distances. A popular measure of the t of the constructed distances to the observed dissimilar-ities is the stress criterion, which must be minimized by numerical optimization. Empirical evidence concerning the existence of nonglobal...

Multidimensional Scaling is a useful tool to represent a nite set in an appropriate graphical display. But MDS can do much more in statistics, classiication and data analysis. It is shown in this contribution that MDS and related methods based on distances provide techniques and solutions to a wide eld of topics: distance based regression with mixed variables and non-linear regression; MDS inte...

MDS (multidimensional scaling) is a technique that enables researchers to uncover the spatial representation or "hidden structure" that underlies and defines behavioral data such as negotiator or disputant perceptions and preferences. Although MDS has wide-ranging theoretical and applied appeal, it has been highly underutilized in the conflict and negotiation literature. In this paper, we seek ...

The self-organizing map (SOM) and its variant, the visualization induced SOM (ViSOM), have been linked with principal manifolds. They have also been shown to yield similar results to multidimensional scaling (MDS). However the exact connection has not yet been established. In this paper we first examine their relationship with (generalized) MDS from their cost functions in the aspect of data vi...