Apriori Algorithm

INFO

A foundational technique in association rule learning used to identify frequent itemsets and derive rules from large transactional datasets.

• Developed by: Agrawal and Srikant (1994)
• Core Principle: A subset of a frequent itemset must also be frequent
• Search Strategy: Level-wise, breadth-first expansion

Workflow

1. Frequent Itemset Generation
   • Expand itemset one item at a time
   • Prune candidates that do not meet the minimum support threshold
2. Rule Derivation
   • Generate rules from frequent itemset
   • Evaluate using metrics:
     • Support: Frequency of itemset in transactions
     • Confidence: Likelihood that consequent appears when antecedent does
     • Lift: Measures strength of association vs. random chance

Code Example

import pandas as pd
from mlxtend.frequent_patterns import apriori, association_rules
 
# Sample transaction dataset
data = {
    'Transaction': [1, 2, 3, 4, 5],
    'Milk': [1, 1, 0, 1, 1],
    'Bread': [1, 0, 1, 1, 1],
    'Butter': [1, 0, 1, 0, 1],
    'Jam': [0, 1, 0, 1, 1]
}
 
# Convert to DataFrame
df = pd.DataFrame(data).set_index('Transaction')
 
# Apply Apriori algorithm
frequent_itemsets = apriori(df, min_support=0.4, use_colnames=True)
 
# Generate association rules
rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.6)
 
# Display results
import ace_tools as tools
tools.display_dataframe_to_user(name="Association Rules", dataframe=rules)

Advantages

• Produces interpretable rules for actionable insights
• Applicable across domains (retail, healthcare, web usage)
• Metrics (support, confidence, lift) help prioritize relationships

Disadvantages

• High computational cost for large datasets
• Poor scalability in high-dimensional spaces
• Requires manual tuning of support/confidence threshold