The total number of distinct
views are of *N*-D data on a 2-D data
space is shown below.

**data-driven (raw):**-
(1 +
*D*)(*N*^{2}-*N*), where*D*is the number of distortion techniques (e.g., random jitter, relaxation) **data-driven (derived):**-
, where k is
the number of distinct derivation algorithms and
*p*_{j}is the number of variations within algorithm*j*(e.g., different distance metrics). **structure-driven (linear ordered):**-
,
where
*FP*is the number of filling patterns,*SP*is the number of methods which introduce white space for separation (space-padding), and*OL*is the number of methods which distort the placement to allow overlaps.

How to select? Factors include:

- Characteristics of data (size, distribution)
- the purpose of the visualization (presentation, confirmation, exploration)
- the specific task(s) at hand (e.g., detection, classification, or measurement of patterns or outliers).
- the skills of the prospective user of the visualization.
- domain knowledge that can lead to "intuitive" mapping
- trade off between efficient screen utilization, the degree of occlusion, and distortion of the values being used to position the glyph
- whether to impose an implicit structure (what comparison metric to use?)