Multiple Uncertainties in Time-Variant Cosmological Particle Data

Though the mediums for visualization are limited, the potential dimensions of a dataset are not. In many areas of scientific study, understanding the correlations between those dimensions and their uncertainties is pivotal to mining useful information from a dataset. Obtaining this insight can necessitate visualizing the many relationships among temporal, spatial, and other dimensionalities of data and its uncertainties. We utilize multiple views for interactive dataset exploration and selection of important features, and we apply those techniques to the unique challenges of cosmological particle datasets. We show how interactivity and incorporation of multiple visualization techniques help overcome the problem of limited visualization dimensions and allow many types of uncertainty to be seen in correlation with other variables

Bibliometric Mapping of the Computational Intelligence Field

In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.neural networks;bibliometric mapping;fuzzy systems;bibliometrics;computational intelligence;evolutionary computation