With the availability of large-scale computing platforms for high-fidelity design and simulations, and instrumentation for gathering scientific as well as business data, increased emphasis is being placed on efficient techniques for analyzing large and extremely high-dimensional data sets. These data sets may comprise discrete attributes, such as those from business processes, information retrieval, and bioinformatics, as well as continuous attributes such as those in scientific simulations, astrophysical measurements, and engineering design.

Analysis of high-dimensional data typically takes the form of extracting correlations between data items, discovering meaningful information in data, clustering data items, and finding efficient representations for clustered data, classification, and event association. Since the volume (and dimensionality) of data is typically large, the emphasis of new algorithms must be on efficiency and scalability to large data sets.