How to generate Association Rules from Frequent Itemsets?
Generating Association Rules from Frequent Itemsets Once the frequent itemsets from transactions in a database D have been found, it is straightforward to generate strong association rules from them (where strong association rules satisfy both minimum support and minimum confidence). This can be done using Eq. (6.4) for confidence, which we show again here for completeness: support_count (AUB) confidence (A⇒ B)=P(B|A) = support_count (A)/support_count (B) The conditional probability is expressed in terms of itemset support count, where support_count (AUB) is the number of transactions containing the itemsets AUB, and support count(A) is the number of transactions containing the itemset A. Based on this equation, association rules can be generated as follows: ■ For each frequent itemset I, generate all nonempty subsets of I. ■ For every nonempty subset s of I, output the rule "s⇒ (I-s)" if support_count(I) / support_count(s)>=min_conf, where min_conf is the minimum confide...