We are drowning in information and starving for knowledge - Rutherford D. Roger

Continual growth in computing power has resulted in the collection of increasing volumes of data. Data Mining is the quantitative science that seeks to extract knowledge from such information. What is interesting about the evolution of data mining research is the increasing complexity of problems being considered and the data structures that arise. Initial challenges of scaling existing algorithms and models to increased volumes have given way to a new generation of problems with complex data structures that require models to be invented from the ground up.

Drawing inspiration from a number of challenging real-world problems, members of this team are inventing new and innovative "statistical learning" tools. These problems often involve complex data structures, such as rare target problems in which a small, valuable part of a population must be identified efficiently, network mining problems, in which the data consist of transactions occurring on a network over time, or monitoring complex processes, which arises when vast quantities of data are collected from a production process such as automotive manufacturing. In many of these areas, new data structures emerge, such as very unbalanced classes in rare target problems, functional data in monitoring, or transactional data structures in network monitoring.

Some of the problems considered by the group have a more methodological focus, rather than arising from a specific scientific problem. These include decision tree modelling and unsupervised learning of non-standard data.