The limitations of using self-organizing maps (SaM) for either clustering/vector quantization (VQ) or multidimensional scaling (MDS) are being discussed by reviewing recent empirical findings and the relevant theory. SaM '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 centroi...

In this paper, we investigate relative localization techniques based on internode distance measurements for small wireless networks. High precision ranging is assumed, which is achieved by using technologies such as ultra-wide band (UWB) ranging. A number of approaches are formulated and compared for relative location estimation, which include the Linear Least Squares (LLS) approach, the Maximu...

Multidimensional scaling (MDS) which is a fundamental problem in data analysis, can be formulated as a nonlinear global optimization problem. In order to solve it, some local, heuristic and global optimization methods are implemented. In another hand, to perform our new heuristic approach, the MDS problem is studied by using a multistart Levenberg-Marquardt method and, also for some small examp...

In this paper we present the methodology of multidimensional scaling problems (MDS) solved by means of the majorization algorithm. The objective function to be minimized is known as stress and functions which majorize stress are elaborated. This strategy to solve MDS problems is called SMACOF and it is implemented in an R package of the same name which is presented in this article. We extend th...

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...