Data Mining and Knowledge Discovery

The Data Mining and Knowledge Discovery program investigates the development and application of advanced algorithmic, architectural and visualization techniques to the problems of deriving, or assisting in the derivation of, knowledge from potentially large volumes of complex data. The three areas use allied techniques and overlapping methodologies although they differ in the quantities of data, the initial information content of that data and the way in which the data is presented to the user.
While the research is widely applicable, of particular interest is our focus on the use of

medical, biomedical, health and clinical data, and
defence intelligence applications
The application of discovery and learning techniques to such datasets is a rewarding but highly challenging area. Not only are the datasets potentially large, complex, heterogeneous, time-varying and of varying quality but there exists a substantial knowledge base which demands a robust collaboration between the data miner and the professional if useful knowledge is to be extracted. Apart from the public benefit of focusing on these areas, we believe that this domain represents perhaps the toughest environment in which to deploy discovery and visualization techniques.

People Involved

A/Prof Paul Calder - Principal Investigator
Dr Aaron Ceglar - Research Fellow
Dr Denise de Vries - Research Fellow
Peter Fule - Adjunct Research Fellow
Ping Liang - Postgraduate Researcher
Dr Carl Mooney - Investigator
Prof John Roddick - Principal Investigator and Contact Person
Anna Shillabeer - Postgraduate Researcher

Recent Publications

More Information

Further information about this research program is available from Prof John Roddick.