Figure 1: An overview of Clustervision on a dataset describing 403 paintings by the “Joy of Painting” artist Bob Ross. (A) Ranked List of Clustering Results shows 16 different clustering results that are sorted by the aggregated quality measures; (B) Projection shows a selected clustering result (highlighted in yellow in (A)) on a projection of data points colored corresponding to corresponding...

The striation model describes the evolution of plasma clouds along and across the geomagnetic field B in the earth ionosphere. The plasma clouds drift across the ionosphere in the presence of an ambient electric field and a neutral wind. In this article we introduce new techniques to extend the simulation tools to the earth magnetic field geometry. The model morevor allows a three dimensional r...

Parallel coordinates, re-orderable matrices, and dendrograms, widely used methods for visual exploration of multivariate data, are systematically integrated in a complementary manner for supporting multi-resolution visual data analysis with an enhanced overviewzdetail exploratory strategy. There are three main topics: (1) dynamic control across resolutions at which data are explored; (2) coordi...

We have analyzed the colorimetric and spectral characteristics of 2600 daylight spectra (global spectral irradiances on a horizontal surface) measured for all sky states during a 2-year period at Granada, Spain. We describe in detail the chromaticity coordinates, correlated color temperatures (CCT), luminous efficacies, and relative UV and IR contents of Granada daylight. The chromaticity coord...

This paper aims for analyzing gait to investigate feature values related to gait transformation using 3D(Dimensional) human data in order to conceal a personal identification. Biometric data including fingerprint, iris, vein, and gait etc., are distinctive characteristics that certificate individuals. Gait is useful even if it is taken from faraway place. A Kinect is used to obtain gait data. 3...

The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN  of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most      relevant information of X. These eigenvectors are called principal components [8]. Ass...

We present a novel approach to studying the diversity of galaxies. It is based on a novel spectral graph technique, that of locally-biased semi-supervised eigenvectors. Our method introduces new coordinates that summarize an entire spectrum, similar to but going well beyond the widely used Principal Component Analysis (PCA). Unlike PCA, however, this technique does not assume that the Euclidean...

in the present study, quantitative relationships between molecular structure and anti-tubercular activity of some 5-methyl/trifluoromethoxy-1 h -indole-2,3-dione-3-thiosemicarbazone derivatives were discovered. the detailed application of an efficient linear method and principal component regression (pcr) for the evaluation of quantitative structure activity relationships of the studied compound...

Formulating a Schubert problem as the solutions to a system of equations in either Plücker space or in the local coordinates of a Schubert cell typically involves more equations than variables. We present a novel primal-dual formulation of any Schubert problem on a Grassmannian or flag manifold as a system of bilinear equations with the same number of equations as variables. This formulation en...

Paleoclimatology requires the analysis of paleo time-series, obtained from a number of independent techniques and instruments, produced by several researchers and/or laboratories. Analytical reasoning techniques that combine the judgment of paleoceanographers with automated reasoning techniques are needed to gain deep insights about commplex climatic phenomena. This paper presents an interactiv...