Flexible Mining of Association Rules

The discovery of association rules showing conditions of data co-occurrence has attracted the most attention in data mining. An example of an association rule is the rule “the customer who bought bread and butter also bought milk,” expressed by T(bread; butter)→T(milk). Let I ={x1,x2,...,xm} be a set of (data) items, called the domain; let D be a collection of records (transactions), where each record, T, has a unique identifier and contains a subset of items in I. We define itemset to be a set of items drawn from I and denote an itemset containing k items to be k-itemset. The support of itemset X, denoted by Ã(X/D), is the ratio of the number of records (in D) containing X to the total number of records in D. An association rule is an implication rule X ⇒ Y, where X; Y ⊆ I and X Y=0. The confidence of X ⇒ Y is the ratio of σ(X Y/D) to σ(X/D), indicating that the percentage of those containing X also contain Y. Based on the userspecified minimum support (minsup) and confidence (minconf), the following statements are true: An itemset X is frequent if σ(X/D)> minsup, and an association rule