A multi-objective evolutionary algorithm for mining quantitative association rules
Diana Martín, Alejandro Rosete Suárez, Jesús Alcalá‐Fdez, Francisco Herrera · 2011
Data mining is most commonly used in attempts to induce association rules from database. Recently, some researchers have suggested the extraction of association rules as a multi-objective problem, removing some of the limitations of current approaches. In this way, we can jointly optimize quality measures which can present different degrees of tradeoff depending on the database used and the type of information can be extracted from it. In this work, we extend the well-known multi-objective evolutionary algorithms NSGA-II to perform an evolutionary learning of the intervals of attributes and a condition selection in order to mine a set of quantitative association rules with a good trade-off between interpretability and accuracy. To do that, this method considers three objectives, maximize the interestingness, comprehensibility and performance. Moreover, this method follows a database-independent approach which does not rely upon minimum support and minimum confidence thresholds. The results obtained over two real-world databases demonstrate the effectiveness of the proposed approach.