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...

MOTIVATION Multidimensional scaling (MDS) is a well-known multivariate statistical analysis method used for dimensionality reduction and visualization of similarities and dissimilarities in multidimensional data. The advantage of MDS with respect to singular value decomposition (SVD) based methods such as principal component analysis is its superior fidelity in representing the distance between...

We propose a graphical method to visualize possible time-varying correlations between fifteen stock market values. The method is useful for observing stable or emerging clusters of stock markets with similar behaviour. The graphs, originated from applying multidimensional scaling techniques (MDS), may also guide the construction of multivariate econometric models. 2011 Elsevier B.V. All rights ...

In this paper, we discuss interactively visualizing hierarchical clustering using multidimensional scaling (MDS) and the minimal spanning tree (MST). We can examine the sequential process leading to agglomerative or divisive hierarchical clustering, compare the di erent agglomerative methods, and detect in uential observations better than is possible with dendrograms.

Multidimensional scaling (MDS) is the process of transforming a set of points in a high-dimensional space to a lower-dimensional one while preserving the relative distances between pairs of points. Although effective methods have been developed for solving a variety of MDS problems, they mainly depend on the vectors in the lower-dimensional space having real-valued components. For some applicat...

The main purpose with this paper is to describe how a psychologically motivated conceptual space can be obtained with MDS (multidimensional scaling) and how it can be expressed in terms of a more primitive “physical” (or mathematical) one. The idea is demonstrated practically with the aid of two experimental pilot studies. The paper is concluded by a critical discussion of the method used.