A novel algorithm for mining couples of enhanced association rules based on the number of output couples and its application

Besides the need for more advanced predictive methods, there is increasing demand for easily interpretable results. Couples of enhanced association rules (a generalization of association rules/apriori/frequent itemsets) are excellent candidates for this task. They can be interpreted in various ways, subgroup discovery being an example. A typical result in rule mining is that there are too low or too many rules in the resulting ruleset. Analysts must usually iterate 5–15 times to get a reasonable number of rules. Inspired by research in a similar area of frequent itemsets to simplify input and parameter-free frequent itemsets, we have proposed a novel algorithm that finds rules based not on parameters like support and confidence but the best rules by a given range of required rule count in output. We propose this algorithm for couples of rules – SD4ft-Miner procedure and benefits from a brand new implementation of methods of mechanizing hypothesis formation in Python called Cleverminer that allows easy implementation of this algorithm. We have verified the algorithm by several applications on eight public data sets. Our original case was a case study, and it was also the reason why we developed the algorithm. However, implementation is in Python, and the algorithm itself can be used on a broader class of methods in any language. The algorithm iterates quickly, in all experiments we needed a maximum of 10 iterations. Possible enhancements to this algorithm are also outlined.