Background: Progressive advances in the measurement of complex multifactorial components of biological processes involving both spatial and temporal domains have made it difficult...
Parallel coordinates have been widely applied to visualize high-dimensional and multivariate data, discerning patterns within the data through visual clustering. However, the effe...
Hong Zhou, Xiaoru Yuan, Huamin Qu, Weiwei Cui, Bao...
: This paper shows an application of two visualization algorithms of multivariate data, U-matrix and Component Planes, in a matter of exploratory analysis of geospatial data. These...
: Probability distribution mapping function, which maps multivariate data distribution to the function of one variable, is introduced. Distributionmapping exponent (DME) is somethi...
This paper presents the concept of Monotone Boolean Function Visual Analytics (MBFVA) and its application to the medical domain. The medical application is concerned with discover...
Boris Kovalerchuk, Florian Delizy, Logan Riggs, Ev...
Real-world data is known to be imperfect, suffering from various forms of defects such as sensor variability, estimation errors, uncertainty, human errors in data entry, and gaps ...
Testing for uniformity of multivariate data is the initial step in exploratory pattern analysis. We propose a new uniformity testing method, which first computes the maximum (sta...
We introduce an approach to visual analysis of multivariate data that integrates several methods from information visualization, exploratory data analysis (EDA), and geovisualizat...
Alan M. MacEachren, Xiping Dai, Frank Hardisty, Di...
We present a novel family of data-driven linear transformations, aimed at visualizing multivariate data in a low-dimensional space in a way that optimally preserves the structure ...
Visualization is a key issue for multivariate data analysis. Multivariate visualization is an active research topic and many efforts have been made in order to find suitable and ...