
Visualizations
XmdvTool
supports four methods for displaying multivariate data: each of them has a flat approach
and a hierarchical approach:
 Scatterplots
 Star
Glyphs
 Parallel
Coordinates
 Dimensional
Stacking
Multivariate
Data
Multivariate
data can be defined as a set of entities E, where the i^{th}
element e_{i }consists of a vector with n varibles,
( x_{i1}, x_{i2}, ..., x _{in} ).
Each variable (dimension) may be independent of or interdependent
with one or more of the other variable. Variables may be discrete
or continuous in nature, or take on symbolic (nominal) values.
Variables also have a scale associated with them, where scales
are defined according to the existence or lack of an ordering
relationship, a distance (interval) metric, and an absolute zero
(origin).
When visualizing
multivariate data, each variable may map to some graphical entity
or attribute. In doing so, the type (discrete, continuous,
nominal) or scale may be changed to facilitate display.
In such situations, care must be taken, as a graphical variable
with a perceived characteristic (type or scale) which is mapped
to a data variable with a different characteristic can lead to
misinterpretation.
Hierarchical
approach
Conventional
multivariate visualization techniques generally do not scale well
with respect to the size of the data sets. To deal with the clustering
and overlapping of large data sets, we extended the flat approaches
to hierarchical approaches. In hierarchical approaches, multiresolutional
views of the data via hierarchical clustering substitute static
views in flat approaches. Variablewidth opacity bands are
used to represent the information of clusters. Proximitybased
coloring highlights the relationships among clusters. Details
of the techniques used in the hierarchical approaches can
be obtained here.
