Which statement about FP-Growth in RapidMiner is true?

• A. It is not used to find frequent patterns.
• B. It is required to create association rules.
• C. It cannot be used with large datasets.
• D. It outputs predictions instead of rules.

Answer: (B)

FP-Growth in RapidMiner is about finding frequent itemsets in transactional data. These frequent itemsets are the building blocks for association rules; a rule like X => Y is derived from a frequent itemset that contains both X and Y, and its strength is measured by support and confidence. FP-Growth does this efficiently by using a compact data structure (the FP-tree) to avoid generating a huge number of candidate itemsets, which makes it a common first step before producing rules. In RapidMiner, you typically run FP-Growth to obtain the frequent itemsets and then apply a rule-generation step to convert those itemsets into actual association rules, using a minimum confidence threshold. So, FP-Growth is the essential precursor in the workflow for creating association rules.