Abstract:
This dissertation addresses the development of new 2-D and 3-D layout
algorithms for statistical visualization purposes. These layouts serve tasks that include
placing near neighbors close together, showing group or cluster membership, allocating
space for glyphs and images used to characterize objects (cases), and approximating
distances between objects. These tasks serve goals that include conveying structure,
facilitating pattern discovery and hypothesis generation, and providing access to detailed
information. The layouts are for human use, so they include considerations of human
perception, cognition, and organizational regularity.
This dissertation targets applications involving the study of cases, variables,
clusters, and other multivariate objects. In these applications the notion of
distances/dissimilarities between objects is important. However, accurate distances can
not be maintained in low dimensional views. Researchers have developed a variety of
layout methods to represent multivariate objects (including data summaries) in low
dimensions. Common layout algorithms include multidimensional scaling, Kohonen self-organizing
maps, Treemaps and spring models. This dissertation compares and contrasts
the new layout algorithms with previous methods, develops new star glyphs, and
demonstrates the new algorithms using multivariate data produced by AIRS
(Atmospheric InfraRed Sounder) and other datasets.
