Abstract
Association rules mining and apriori algorithm are very known tools of data mining since the 1990s. However, enhanced association rules were introduced more than 20 years earlier as a tool of mechanizing hypothesis formation – an approach to exploratory data analysis. Various, both practical and theoretical results, were achieved in relation to enhanced association rules that provide much more general rules than apriori. A short overview of these results is presented. A new implementation of an analytical procedure dealing with enhanced association rules is introduced as well as novel algorithm to find association rules not by setting quantifiers but by setting expected number of association rules which corresponds with typical data scientists’ needs.
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Notes
- 1.
In fact, not all rules are verified when optimization is in place but result is the same as if all rules would be verified.
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Máša, P., Rauch, J. (2023). Enhanced Association Rules and Python. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13811. Springer, Cham. https://doi.org/10.1007/978-3-031-25891-6_10
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