The key strength of association rule mining is that it searches the space of rules completely by examining all patterns that occur frequently in the data. However, the main disadvantage is that the number of association rules it finds is often very large. Moreover, many rules are redundant because they can be naturally explainedbyotherrules.Thismayhinderthediscoveryprocessandtheinterpretabilityof the results. The objective of this work is to filter out these redundant rules and provide
the user with a small set of rules that are sufficient to capture the essential underlying structure of the data.