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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)
Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proc. of the ACM SIGMOD Int. Conf. on Management of Data, Dallas, Texas, USA, pp. 1–12 (2000)
David, M.: Beasley Python Essential Reference. New Riders Publishing, Indianapolis (2000)
Commonwealth Department of Human Services and Health, Medicare Benefits Schedule Book. Australian Government Publishing Service, Canberra (2000)
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of the, ACM SIGMOD Conference, Washigton DC, USA (1994)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. IBM Almaden Research Center, San Jose, CA (1994)
Hegland, M.: Data Mining Techniques, ANU, Canberra (2000)
Edward, B.: Episodes of Care, ProfSoft Inc., Cambridge, Massachusets, USA (1999)
Semenova, T., Hegland, M., Graco, W., Williams, G.: Effectiveness of Mining Association Rules in Large Health Databases. CalPoly: Technical Report No.CPSLOCSC- 01-03. In: ICDM 2001: Workshop on Integrating Data Mining and Knowledge Management, San Jose, California (2001)
Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical foundations. Springer, Heidelberg (1999)
Duquenne, V., Guiges, J.-L.: Famille minimale d’implications informatives résultant d’un tableau de données binaires. Math. et Sci.Hum. 24(95), 5–18 (1986)
Pasquir, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Proc. ICDT Conf., pp. 398–416 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Semenova, T., Hegland, M., Graco, W., Williams, G. (2004). Conceptual Mining of Large Administrative Health Data. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_78
Download citation
DOI: https://doi.org/10.1007/978-3-540-24775-3_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22064-0
Online ISBN: 978-3-540-24775-3
eBook Packages: Springer Book Archive