Which term refers to a set of rules that describe associations between items in large datasets?

Which term refers to a set of rules that describe associations between items in large datasets?

• A. Clustering methods
• B. Neural networks (NNs)
• C. Feedforward neural network (FNN)
• D. Association rules

Answer: (D)

Association rules describe relationships between items in large datasets as explicit if-then patterns, such as “If a customer buys X, they also buy Y.” They’re discovered by mining transaction data and evaluated with metrics like support (how often the combination appears), confidence (how often Y occurs when X occurs), and lift (how much more likely Y is when X occurs than overall). This makes them especially useful for market basket analysis and cross-selling, where you want clear recommendations derived from actual co-occurrences. The other terms refer to different approaches: clustering groups items or customers without producing explicit item-to-item rules, and neural networks—including feedforward networks—learn complex patterns but don’t typically yield straightforward association rules.

Association rules describe relationships between items in large datasets as explicit if-then patterns, such as “If a customer buys X, they also buy Y.” They’re discovered by mining transaction data and evaluated with metrics like support (how often the combination appears), confidence (how often Y occurs when X occurs), and lift (how much more likely Y is when X occurs than overall). This makes them especially useful for market basket analysis and cross-selling, where you want clear recommendations derived from actual co-occurrences. The other terms refer to different approaches: clustering groups items or customers without producing explicit item-to-item rules, and neural networks—including feedforward networks—learn complex patterns but don’t typically yield straightforward association rules.