How does association rule mining scale to big data environments?

It uses distributed algorithms and pruning strategies to handle large datasets.

Traditional Apriori becomes inefficient at scale due to candidate explosion. Big data systems use parallel FP-Growth, MapReduce-based Apriori, and distributed support counting. Pruning strategies reduce search space, while partitioning enables parallel frequent itemset mining.