One of the most popular graph drawing methods is based of achieving graphtheoretic target ditsances. This method was used by Kamada and Kawai [15], who formulated it as an energy optimization problem. Their energy is known in the multidimensional scaling (MDS) community as the stress function. In this work, we show how to draw graphs by stress majorization, adapting a technique known in the MDS...

1 Abstract Few studies have explored the possibility of mining Web hyperlink data for e-commerce or business information. This study is an attempt to fill this gap. The project selected a group of telecommunications equipment companies and collected data on the number of links pointing to the company Websites (inlinks) and the company’s revenue. A significant correlation between the two variabl...

Further tests were provided of an exemplar-similarity model for relating the identification and categorization of separable-dimension stimuli (Nosofsky, 1986). On the basis of confusion errors in an identification paradigm, a multidimensional scaling (MDS) solution was derived for a set of 16 separable-dimension stimuli. This MDS solution was then used in conjunction with the exemplar-similarit...

Two experiments are reported in which linear separability was investigated in superordinate natural language concept pairs (e.g., toiletry-sewing gear). Representations of the exemplars of semantically related concept pairs were derived in two to five dimensions using multidimensional scaling (MDS) of similarities based on possession of the concept features. Next, category membership, obtained ...

Software cost estimation is a crucial task in software project portfolio decisions like start scheduling, resource allocation, or bidding. A variety of estimation methods have been proposed to support estimators. Especially the analogy-based approach—based on a project’s similarities with past projects—has been reported as both efficient and relatively transparent. However, its performance was ...

We give a tutorial overview of several geometric methods for dimension reduction. We divide the methods into projective methods and methods that model the manifold on which the data lies. For projective methods, we review projection pursuit, principal component analysis (PCA), kernel PCA, probabilistic PCA, canonical correlation analysis, oriented PCA, and several techniques for sufficient dime...

Y chromosomal STRs show sufficient variability among individduals in a population and a high degree of geographical differentiation, such that their polymorphic character makes them especially suited for population genetic studies. To investígate the polymorphism of a set of 17 Y-STR loci in northern China, we genotyped the 17 Y chromosomal STR loci in a population sample of 377 unrelated males...

Localization, an important challenge in wireless sensor networks, is the process of sensor nodes self-determining their position. The difficulty encountered is in cost-effectively providing acceptable accuracy in localization. The potential for the deployment of high density networks in the near future makes scalability a critical issue in localization. In this paper we propose Cluster-based Lo...

| Multidimensional Scaling (MDS) techniques always pose the problem of analysing a large number N of points, without collecting all N(N?1) 2 possible interstimuli dissimilarities, and while keeping satisfactory solutions. In the case of metric MDS, it was found that a theoretical minimum of appropriate 2N ?3 exact Euclidean distances are suf-cient for the unique representation of N points in a ...

Distance-based methods in machine learning and pattern recognition have to rely on a metric distance between points in the input space. Instead of specifying a metric a priori, we seek to learn the metric from data via kernel methods and multidimensional scaling (MDS) techniques. Under the classification setting, we define discriminant kernels on the joint space of input and output spaces and p...