Article ID: | iaor20002537 |
Country: | Australia |
Volume: | 41 |
Issue: | 3 |
Start Page Number: | 255 |
End Page Number: | 275 |
Publication Date: | Sep 1999 |
Journal: | Australian and New Zealand Journal of Statistics |
Authors: | Mackinnon Murray J., Glick Ned |
Keywords: | datamining |
Data mining seeks to extract useful, but previously unknown, information from typically massive collections of non-experimental, sometimes non-traditional data. From the perspective of statisticians, this paper surveys techniques used and contributions from fields such as data warehousing, machine learning from artificial intelligence, and visualization as well as statistics. It concludes that statistical thinking and design of analysis, as exemplified by achievements in clinical epidemiology, may fit well with the emerging activities of data mining and ‘knowledge discovery in databases’ (DM&KDD).