A unified approach for discovery of interesting association rules in medical databases

Authors:
Harleen Kaur;Siri Krishan Wasan;Ahmed Sultan Al-Hegami;Vasudha Bhatnagar
Affiliations:
Department of Mathematics, Jamia Millia Islamia, New Delhi, India;Department of Mathematics, Jamia Millia Islamia, New Delhi, India;Department of Computer Science, Sana'a University, Sana'a, Yemen;Department of Computer Science, University of Delhi, New Delhi, India
Venue:
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Year:
2006

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Abstract

Association rule discovery is an important technique for mining knowledge from large databases. Data mining researchers have studied subjective measures of interestingness to reduce the volume of discovered rules and to improve the overall efficiency of the knowledge discovery in databases process (KDD). The objective of this paper is to provide a framework that uses subjective measures of interestingness to discover interesting patterns from association rules algorithms. The framework works in an environment where the medical databases are evolving with time. In this paper we consider a unified approach to quantify interestingness of association rules. We believe that the expert mining can provide a basis for determining user threshold which will ultimately help us in finding interesting rules. The framework is tested on public datasets in medical domain and results are promising.