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...

We investigate whether the Pairwise Rating Method (PRaM) and Spatial Arrangement (SpAM) yield multidimensional scaling (MDS) solutions of comparable dimensionality. Across three studies that included twelve semantic categories with varying numbers both pictorial verbal exemplars, we did not find consistent dimensionality differences between two similarity measurement methods. The results allevi...

Transportation systems exist within at least two types of space. One is the apparent geographic space, but equally important is the time–space implied by the travel time relations created by the system. Differences between the geographic and time–spaces are properties induced by the transportation system. Methods for time–space transformations of geographic space to explore, visualize and analy...

Multidimensional scaling (MDS) analysis is a dimension-reduction technique that is used to estimate the coordinates of a set of objects. However, not every criterion used in multidimensional scaling is equally and precisely weighted in the real world. To address this issue, we use fuzzy analytic hierarchy process (FAHP) to determine the weighting of subjective/perceptive judgments for each crit...

A common task in data mining is the visualization of multivariate objects using various methods, allowing human observers to perceive subtle inter-relations in the dataset. Multidimensional scaling (MDS) is a well known technique used for this purpose, but it due to its computational complexity there are limitations on the number of objects that can be displayed. Combining MDS with a clustering...

Waves of globalization reflect the historical technical progress and modern economic growth. The dynamics of this process are here approached using the multidimensional scaling (MDS) methodology to analyze the evolution of GDP per capita, international trade openness, life expectancy, and education tertiary enrollment in 14 countries. MDS provides the appropriate theoretical concepts and the ex...