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.

This paper applies fast sparse multidimensional scaling (MDS) to a large graph of music similarity, with 267K vertices that represent artists, albums, and tracks; and 3.22M edges that represent similarity between those entities. Once vertices are assigned locations in a Euclidean space, the locations can be used to browse music and to generate playlists. MDS on very large sparse graphs can be e...

We present an approximation algorithm for Multidimensional Scaling (MDS) for use with large datasets and interactive applications. MDS describes a class of dimensionality reduction techniques that takes a dissimilarity matrix as input. It is often used as a tool for understanding relative measurements when absolute measurements are not available. MDS is also used for visualizing high-dimensiona...

In this work, we propose MDS-Tree and MDS-Matrix as novel high dimensional data visualization methods to gain insight in both the data aspect and dimension aspect of the data. Dimension metrics of the high dimensional dataset are first computed to create a hierarchy. In an MDS-Tree, each node is an MDS projection of the original data items on a specific subset of dimensions associated with the ...

Knowledge mining from immense datasets requires fast, reliable and affordable tools for their visual and interactive exploration. Multidimensional scaling (MDS) is a good candidate for embedding of high-dimensional data into visually perceived 2-D and 3-D spaces. We focus here on the way to increase the computational performance of MDS in the context of interactive, hierarchical, visualization ...

This paper applies fast sparse multidimensional scaling (MDS) to a large graph of music similarity, with 267K vertices that represent artists, albums, and tracks; and 3.22M edges that represent similarity between those entities. Once vertices are assigned locations in a Euclidean space, the locations can be used to browse music and to generate playlists. MDS on very large sparse graphs can be e...