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Conceptual mining of large administrative health data

Semenova, Tatiana; Hegland, Markus; Graco, Warwick; Williams, Graham


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 undustry specifics. For conceptual mining, the classic pattern-growth methods are found limited due to their great resource consumption. As an alternative, we propose a pattern splitting technique which delivers as complete and compact knowledge about the data as the pattern-growth techniques, but is found...[Show more]

CollectionsANU Research Publications
Date published: 2004
Type: Conference paper
Source: Advances in Knowledge Discovery and Data Mining. 8th Pacific-Asia Conference, PAKDD 2004 Proceedings


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