What association rule learning is

Association rules find patterns like:

- {bread, butter} → {milk}

- {bread, butter} → {milk}

Common use-case: market basket analysis.

Key metrics

Support

How often an itemset appears:

• support({A,B}{A,B}) = fraction of transactions containing A and B

Confidence

How often B appears when A appears:

• confidence(A→B) = support(A,B) / support(A)

Lift

How much more likely B is when A happens, compared to baseline:

• lift(A→B) = confidence(A→B) / support(B)

Lift > 1 suggests a meaningful association.

Apriori idea

Apriori uses the principle:

• if an itemset is frequent, all its subsets are frequent
• if an itemset is infrequent, all supersets are infrequent

This reduces search.

  flowchart TD
  A[Find frequent 1-itemsets] --> B[Generate candidate 2-itemsets]
  B --> C[Prune infrequent]
  C --> D[Generate candidate 3-itemsets]
  D --> E[Repeat]

Practical note

Apriori is often implemented with specialized libs (e.g., mlxtendmlxtend). You can learn the concepts without installing anything.

Mini-checkpoint

If a rule has confidence 0.9 but lift 1.0, what does that mean?

• It’s not actually more informative than baseline; B is common anyway.