Module mining 

Pattern mining algorithms for association rule discovery.

This module provides algorithms for discovering patterns in transactional data, particularly association rules used in market basket analysis.

Algorithms

• Apriori: Frequent itemset mining and association rule generation

Example

use aprender::mining::Apriori;

// Market basket transactions (each transaction is a set of item IDs)
let transactions = vec![
    vec![1, 2, 3],    // Transaction 1: items 1, 2, 3
    vec![1, 2],       // Transaction 2: items 1, 2
    vec![1, 3],       // Transaction 3: items 1, 3
    vec![2, 3],       // Transaction 4: items 2, 3
];

// Find frequent itemsets with minimum support 0.5 (50%)
let mut apriori = Apriori::new()
    .with_min_support(0.5)
    .with_min_confidence(0.7);

apriori.fit(&transactions);

// Get association rules
let rules = apriori.get_rules();
for rule in rules {
    println!("{:?} => {:?} (conf={:.2}, lift={:.2})",
        rule.antecedent, rule.consequent, rule.confidence, rule.lift);
}

Structs

Apriori

Apriori algorithm for frequent itemset mining and association rule generation.

AssociationRule

Association rule: antecedent => consequent