We describe an attempt to overcome information overload through information visualization — in a particular domain, group memory. A brief review of information visualization is followed by a brief description of our methodology. We Ž . discuss our system, which uses multidimensional scaling MDS to visualize relationships between documents, and which Ž . we tested on 60 subjects, mostly students...

Multidimensional scaling (MDS) is a class of projective algorithms traditionally used to produce twoor three-dimensional visualizations of datasets consisting of multidimensional objects or interobject distances. Recently, metric MDS has been applied to the problems of graph embedding for the purpose of approximate encoding of edge or path costs using node coordinates in metric space. Several a...

Multidimensional scaling (MDS) is a multivariate statistical technique that can be used to define subsystems of functionally connected brain regions based on the analysis of functional magnetic resonance imaging (fMRI) data. Here we introduce three-way multidimensional scaling as a method for the analysis of a group of fMRI data, which yields both a generic interregional configuration in low-di...

The canonical application of multidimensional scaling (MDS) methods has been to color dissimilarities, visualizing these as distances in a low-dimensional space. Some questions remain: How well can the locations of stimuli in color space be recovered when data are sparse, and how well can systematic individual variations in perceptual scaling be distinguished from stochastic noise? We collected...

Self-Organizing Feature-Mapping (SOFM) algorithm is frequently used for visualization of high-dimensional (input) data in a lower-dimensional (target) space. This algorithm is based on adaptation of parameters in local neighborhoods and therefore does not lead to the best global visualization of the input space data clusters. SOFM is compared here with alternative methods of global visualizatio...

Data visualization is a core approach for understanding data specifics and extracting useful information in a simple and intuitive way. Visual data mining proceeds by projecting multidimensional data onto two-dimensional (2D) or three-dimensional (3D) data, e.g., through mathematical optimization and topology preserved in multidimensional scaling (MDS). However, this projection does not necessa...

We discuss methodology for multidimensional scaling (MDS) and its implementation in two software systems, GGvis and XGvis. MDS is a visualization technique for proximity data, that is, data in the form of N × N dissimilarity matrices. MDS constructs maps (“configurations,” “embeddings”) in IRk by interpreting the dissimilarities as distances. Two frequent sources of dissimilarities are high-dim...