Although the use of ions or glyphs is a common way of displaying multivariate data, these techniques do not scale well with dataset size. Displaying large amounts of data requires the placement of many icons in the display often resulting in images which are cluttered and where important patterns and structures are obscured. In this paper we present an adaptive multi-scale technique that uses concepts of abstraction and importance, combined with icon display that helps alleviate the problem of visual clutter. Abstraction functions are used to transform and reduce the data, importance functions are used to identify important areas within the data. Abstractions of abstractions are computed forming a multi-scale representation of the data which is used to display the data. The data is displayed by distributing a specified number of icons through it using the computed importance values. The multi-scale structure ensures that relative importance is maintained through the distribution of icons in the image. We demonstrate this technique by applying it to multivariate data defined over two dimensions. We show how a range of abstraction functions can be used with importance and display methods to display and explore a number of example datasets.