XmdvTool supports four methods for displaying multivariate data: each of them has a flat approach and a hierarchical approach:

  1. Scatterplots
  2. Star Glyphs
  3. Parallel Coordinates
  4. Dimensional Stacking

Multivariate Data

Multivariate data can be defined as a set of entities E, where the ith  element ei consists of a vector with n varibles, ( xi1, xi2, ..., 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, multi-resolutional views of the data via hierarchical clustering substitute static views in flat approaches.  Variable-width opacity bands are used to represent the information of clusters. Proximity-based coloring highlights the relationships among clusters.  Details of  the techniques used in the hierarchical approaches can be obtained here.