The limitations of using self-organizing maps (SOM) for either clustering/vector quantization (VQ) or multidimensional scaling (MDS) are being discussed by reviewing recent empirical ndings and the relevant theory. SOM's remaining ability of doing both VQ and MDS at the same time is challenged by a new combined technique of online K-means clustering plus Sammon mapping of the cluster centroids....

A common task in data mining is the visualization of multivariate objects on scatterplots, allowing human observers to perceive subtle inter-relations in the dataset such as outliers, groupings or other regularities. Leastsquares multidimensional scaling (MDS) is a well known Exploratory Data Analysis family of techniques that produce dissimilarity or distance preserving layouts in a nonlinear ...

This paper examines modern economic growth according to the multidimensional scaling (MDS) method and state space portrait (SSP) analysis. Electing GDP per capita as the main indicator for economic growth and prosperity, the long-run perspective from 1870 to 2010 identifies the main similarities among 34 world partners’ modern economic growth and exemplifies the historical waving mechanics of t...

The purpose of this study is to investigate the features of emotional speech by means of multidimensional scaling procedure(MDS) based on visual-perceived similarity of vocal parameters. We extracted three vocal parameters (pitch, intensity and spectrogram) from speeches expressed emotions. Three researchers grouped together the cards of parameters in view of visual similarity. According to the...

This article is an introduction to Cluster Optimized Proximity Scaling (COPS) aimed at practitioners, as well a tutorial on the usage of corresponding R package cops. COPS variant multidimensional scaling (MDS) that aims providing clustered configuration while still representing multivariate dissimilarities faithfully. It subsumes most popular MDS versions special cases. We illustrate ideas, us...

The term ‘Multidimensional Scaling’ or MDS is used in two essentially different ways in statistics (de Leeuw & Heiser 1980a). MDS in the wide sense refers to any technique that produces a multidimensional geometric representation of data, where quantitative or qualitative relationships in the data are made to correspond with geometric relationships in the representation. MDS in the narrow sense...

We explored differences in the mental representation of facial identity between 8-year-olds and adults. The 8-year-olds and adults made similarity judgments of a homogeneous set of faces (individual hair cues removed) using an "odd-man-out" paradigm. Multidimensional scaling (MDS) analyses were performed to represent perceived similarity of faces in a multidimensional space. Five dimensions acc...

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 discuss our system, which uses multidimensional scaling (MDS) to visualize relationships between documents, and which we tested on 60 subjects, mostly students. We fou...

Self-organizing map (SOM) and multidimensional scaling (MDS) are the methods of data analysis that reduce dimensionality of the input data and visualize the structure of multidimensional data by means of projection. Both methods are widely used in different research areas. In the studies of emotion vocabulary and other psycho-lexical surveys the MDS has been prevalent. In this paper both of the...

Multidimensional scaling techniques (MDS) are a vibrant area of research with much development and advancement in last few decades. In this paper we focus on an interactive 2-dimensional interface for creating a similarity space for audio. Classical MDS techniques can place great demands upon participants overwhelming their sensory and cognitive abilities to make choices across such datasets us...