Good prospects ahead for data mining
Box 1 | Data mining the stars – from Canberra to the cosmos
Astronomers produce vast quantities of data. In one particular study, data have been collected every night for several years on the fluctuating intensities of around 20 million stars. It would take many years for such a mass of data to be sorted out by hand.
Already, astronomers know of many different kinds of star, from quasars to black holes, from red giants to white dwarves. But they are always on the lookout for new star types, since these might aid our understanding of the processes that shape our universe. So astronomers have enlisted the assistance of data miners at the Cooperative Research Centre for Advanced Computational Systems based in Canberra. Their task is to sort through the data and identify clusters of stars in the hope that some of these may represent star types that were previously unknown.
This is just one project being conducted within the data-mining program at the CRC. Others are focused on solving business problems in sectors such as retail, finance, health care, government, telecommunications, and manufacturing. These include the detection of fraud against Medicare and the Australian Tax Office, and the modelling of motor vehicle claim frequency and cost on behalf of an insurance company.
Box
Box 2. Challenges in data mining
Related site
Data Mining Program (Cooperative Research Centre for Advanced Computational Systems, Australia)
Other examples of scientific data mining
Data mining in a scientific environment (Australian Nuclear Science and Technology Organisation)
New approaches to using scientific data – statistics, data mining and related technologies in research and research training (J.H. Maindonald, Australian National University)
Data mining from a statistical perspective (John Maindonald, Australian National University)
Sapphire: Large scale data mining and pattern recognition (Lawrence Livermore National Laboratory, USA)
Page updated September 2004.