Which data mining method is commonly used to uncover associations among items, such as rules like X implies Y?

• A. Association Rules
• B. Clustering
• C. Regression
• D. Decision Trees

Answer: (A)

Association rule mining is used to uncover relationships between items in a dataset, expressing them as rules like X implies Y. It focuses on finding patterns of co-occurrence and quantifying them with metrics such as support (how often X and Y occur together), confidence (how often Y occurs when X occurs), and lift (how much more likely Y is with X than by chance). This approach directly answers questions like which items tend to appear together in transactions, making it ideal for tasks such as market basket analysis. Other methods serve different purposes: clustering groups similar records, regression predicts a numerical outcome, and decision trees create prediction rules for a target variable, rather than discovering item-to-item associations. So, the method described is the one designed specifically to reveal those X implies Y type relationships.