The extraction of relevant and meaningful information out of high-dimensional data is notoriously complex and cumbersome. The curse of dimensionality is a popular way of stigmatizing the whole set of troubles encountered in high-dimensional data analysis; finding relevant projections, selecting meaningful dimensions, and getting rid of noise, being only a few of them. Multi-dimensional data visualization also carries its own set of challenges like, above all, the limited capability of any technique to scale to more than a handful of data dimensions.

The actual focus of our group’s work is to use Visual Analytics in discovering patterns of high-dimensional data.