Conceptual mining of large administrative health data

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 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 to be more efficient. Show more