ASSOCIATION RULE TECHNIQUE

Let us first understand the basic methodology of the Association Rule Theory. Let I be a set of literals called items, D be a set of transactions where each transaction T is a set of items such that T is a subset of I. If X is a set of some items in I and if X is a subset of T then we can say that T contains X. Association Rule Theory is a proposition of the form X => Y where X, Y both are subsets of I and X∩Y = Φ. For the rule X => Y, if c% of the transactions in D that contain X also contain Y then we can say that for that rule the confidence is c and the support for the rule is s if s% of the transactions in D contain X U Y. Itemsets with minimum support are called as large itemsets and those with support less than minimum support are referred to as small itemsets. For a given set of transactions D, the problem of mining association rules is to generate all the association rules that have confidence and support greater than the user specified minimum confidence and support.