  Visualization of high-dimensional data is counter-intuitive using
conventional graphs. Parallel coordinates are proposed as an alternative to
explore multivariate data more effectively. However, it is difficult to extract
relevant information through the parallel coordinates when the data are
high-dimensional with thousands of lines overlapping. The order of the axes
determines the perception of information on parallel coordinates. Thus, the
information between attributes remain hidden if coordinates are improperly
ordered. Here we propose a general framework to reorder the coordinates. This
framework is general to cover a large range of data visualization objective. It
is also flexible to contain many conventional ordering measures. Consequently,
we present the coordinate ordering binary optimization problem and enhance
towards a computationally efficient greedy approach that suites
high-dimensional data. Our approach is applied on wine data and on genetic
data. The purpose of dimension reordering of wine data is highlighting
attributes dependence. Genetic data are reordered to enhance cluster detection.
The presented framework shows that it is able to adapt the measures and
criteria tested.
