The Association Rules model seeks to find frequent connections between observations in a data set.

• A. True
• B. False
• C. Often
• D. Sometimes

Answer: (B)

The main idea is to uncover patterns where items appear together in the same transactions and to quantify how often those co-occurrences happen. Association rule mining begins by finding frequent itemsets—groups of items that occur together in a transaction above a minimum support threshold. From these, it generates rules of the form if X then Y and keeps only those with enough confidence (and sometimes lift) to be considered reliable. This focuses on relationships among items within a transaction, not just general connections between separate observations across the dataset. That’s why the statement is not accurate: it’s about co-occurrence of items and the strength of those implications, rather than broad connections between observations. For example, in market basket data, if many customers buy bread and butter together, we might derive a rule like if bread is bought, butter is likely to be bought as well, with a specified confidence.