Association Rule Mining Cheat Sheet

Explains support, confidence, and lift metrics along with the Apriori and FP-Growth algorithms for discovering frequent itemsets and association rules.

2 PagesIntermediateFeb 25, 2026

Key Metrics

The three numbers every association rule is judged on.

Support- P(A and B): fraction of all transactions containing both items
Confidence- P(B|A): fraction of transactions with A that also contain B
Lift- confidence(A->B) / support(B); lift > 1 means A and B co-occur more than chance
Antecedent / consequent- "If A (antecedent) then B (consequent)" -- the left- and right-hand sides of a rule
Frequent itemset- A set of items whose support meets a minimum threshold

Apriori Algorithm

Mine frequent itemsets and rules from one-hot encoded transactions with mlxtend.

python

from mlxtend.frequent_patterns import apriori, association_rules# df: one-hot encoded, rows = transactions, cols = items (True/False)frequent_itemsets = apriori(df, min_support=0.02, use_colnames=True)rules = association_rules(frequent_itemsets, metric='lift', min_threshold=1.0)rules = rules.sort_values('lift', ascending=False)print(rules[['antecedents', 'consequents', 'support', 'confidence', 'lift']].head())

FP-Growth Algorithm

Faster alternative to Apriori that avoids repeated database scans via an FP-tree.

python

from mlxtend.frequent_patterns import fpgrowthfrequent_itemsets = fpgrowth(df, min_support=0.02, use_colnames=True)print(frequent_itemsets.sort_values('support', ascending=False).head(10))

Common Use Cases

Where market-basket analysis shows up in practice.

Market basket analysis- "Customers who bought X also bought Y" recommendations in retail
Cross-selling- Bundling frequently co-purchased products in promotions
Web usage mining- Finding sequences of pages frequently visited together
Fraud detection- Discovering unusual co-occurring transaction patterns

Pro Tip

A high-lift rule with very low support is often just noise from a handful of transactions -- always check support alongside lift before acting on a rule.

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