Summary
Nowadays mining association rules in a database is a quite simple task; many algorithms have been developed to discover regularities in data. The analysis and the interpretation of the discovered rules are more difficult or almost impossible, given the huge number of generated rules. In this paper we propose a three step strategy to select only interesting association rules after the mining process. The proposed approach is based on the introduction of statistical tests in order to prune logical implications that are not significant.
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Notes
2The Bonferroni effect related to multiple tests performed on the same data set is under investigation but it is expected not to modify substantially the structure of the proposed strategy and the idea behind it.
3This constraint should be no problem in a Data Mining application.
4An α value equal to 0.05 is a typical choice in statistical hypothesis tests.
5The rules generated contain at most two items in the antecedent and one item in the consequence.
References
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This research was supported by“Ricerca Dipartimentale 1999” grant (Prof. C. Lauro). We are very grateful to Prof. Carlo Lauro for his helpful suggestions.
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Bruzzese, D., Davino, C. Statistical Pruning of Discovered Association Rules. Computational Statistics 16, 387–398 (2001). https://doi.org/10.1007/s001800100074
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DOI: https://doi.org/10.1007/s001800100074