Discovering patterns of medical practice in large administrative health databases

Authors:
Tatiana Semenova
Affiliations:
Computer Sciences Laboratory, Australian National University, Canberra ACT 0200, Australia
Venue:
Data & Knowledge Engineering
Year:
2004

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Abstract

Health databases are characterised by large number of records, large number of attributes and mild density. This encourages data miners to use methodologies that are more sensitive to health industry specifics. For conceptual mining, the classic pattern-growth methods are found limited due to their great resource consumption. As an alternative, we propose a technique that uses some of the properties of graphs. Such a technique delivers as complete and compact knowledge about the data as the pattern-growth techniques, but is found to be more efficient.