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.
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Kaur, H., Wasan, S.K., Al-Hegami, A.S., Bhatnagar, V. (2006). A Unified Approach for Discovery of Interesting Association Rules in Medical Databases. In: Perner, P. (eds) Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining. ICDM 2006. Lecture Notes in Computer Science(), vol 4065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11790853_5
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DOI: https://doi.org/10.1007/11790853_5
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