Which operator is used to generate association rules in RapidMiner?

• A. FP-Growth
• B. Apriori
• C. K-Means
• D. SVM

Answer: (A)

Association rule mining starts by finding frequent itemsets—the groups of items that appear together in transactions more often than a threshold. FP-Growth is the operator in RapidMiner that efficiently mines these frequent itemsets, especially on large datasets, without generating a huge number of candidate itemsets. Once you have the frequent itemsets from FP-Growth, you can derive association rules by applying a rule-generation step that uses those itemsets with specified support and confidence thresholds. The other options don’t fit this purpose: K-Means is for clustering, and SVM is for classification, so they aren’t used to derive association rules. Apriori also finds frequent itemsets and could generate rules, but FP-Growth is typically favored in RapidMiner for its speed and scalability, making it the best fit in this context.