Abstract
The quality of discovered association rules is commonly evaluated by interestingness measures (commonly support and confidence) with the purpose of supplying subsidies to the user in the understanding and use of the new discovered knowledge. Low-quality datasets have a very bad impact over the quality of the discovered association rules, and one might legitimately wonder whether a so-called “interesting” rule noted LHS -> RHS is meaningful when 30 % of LHS data are not up-to-date anymore, 20% of RHS data are not accurate, and 15% of LHS data come from a data source that is well-known for its bad credibility. In this paper we propose to integrate data quality measures for effective and quality-aware association rule mining and we propose a cost-based probabilistic model for selecting legitimately interesting rules. Experiments on the challenging KDD-CUP-98 datasets show for different variations of data quality indicators the corresponding cost and quality of discovered association rules that can be legitimately (or not) selected.
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Berti-Équille, L. (2006). Quality-Aware Association Rule Mining. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_51
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DOI: https://doi.org/10.1007/11731139_51
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