Large amounts of data are collected routinely in business, government departments and research and development organisations. They are typically stored in large data warehouses or data bases. For data mining tasks suitable data has to be extracted, linked, cleaned and integrated with external sources. Further data analysis is required to find accurate, useful and understandable information. This extraction of information provides formidable challenges as the underlying concepts of data mining are far more complex than traditional data base query or reporting.
Specific approaches are required which can handle the computational challenges that originate from the size and complexity of data sets used in data mining.
The ANU Data Mining Group specialises in developing computational approaches to data mining which can deal with large scale high dimensional data sets. We are applying our technologies to real world consultancies. Technology development includes scalable parallel algorithms, handling of large complex data sets and scripting tools for automatisation of routine tasks. We have consultancy experience in health (Medicare data), astronomy and chemistry. The main research focus of the group is on predictive modelling, in particular, sparse grids, additive models, high dimensional wavelet smoothing and time series analysis.
We are a group of researches from the ANU Mathematical Sciences Institute and the Department of Computer Science. We are working in collaboration with researchers from both at the ANU and other universities, as well as various industrial partners.