Association rule mining finds interesting associations and correlation relationships among large sets of data items. Association rules show attribute value conditions that occur frequently together in a given data set. A typical example of association rule mining is Market Basket Analysis.

Data is collected using bar-code scanners in supermarkets. Such market basket databases consist of a large number of transaction records. Each record lists all items bought by a customer on a single purchase transaction. Managers would be interested to know if certain groups of items are consistently purchased together. They could use this data for adjusting store layouts (placing items optimally with respect to each other), for cross-selling, for promotions, for catalog design, and to identify customer segments based on buying patterns.

Association rules provide information of this type in the form of if-then statements. These rules are computed from the data and, unlike the if-then rules of logic, association rules are probabilistic in nature.

In addition to the **antecedent** (if) and the **consequent** (then), an association rule has two numbers that express the degree of uncertainty about the rule. In association analysis, the antecedent and consequent are sets of items (called itemsets) that are disjoint (do not have any items in common).

The first number is called the ** support** for the rule. The support is simply the number of transactions that include all items in the antecedent and consequent parts of the rule. The support is sometimes expressed as a percentage of the total number of records in the database.)

The other number is known as the confidence of the rule. ** Confidence** is the ratio of the number of transactions that include all items in the consequent, as well as the antecedent (the support) to the number of transactions that include all items in the antecedent.

For example, if a supermarket database has 100,000 point-of-sale transactions, out of which 2,000 include both items A and B, and 800 of these include item C, the association rule "If A and B are purchased, then C is purchased on the same trip," has a support of 800 transactions (alternatively 0.8% = 800/100,000), and a confidence of 40% (=800/2,000). One way to think of support is that it is the probability that a randomly selected transaction from the database will contain all items in the antecedent and the consequent, whereas the confidence is the conditional probability that a randomly selected transaction will include all the items in the consequent, given that the transaction includes all the items in the antecedent.

**Lift **is one more parameter of interest in the association analysis. Lift is nothing but the ratio of Confidence to Expected Confidence. Using the above example, expected Confidence in this case means, "confidence, if buying A and B does not enhance the probability of buying C." It is the number of transactions that include the consequent divided by the total number of transactions. Suppose the number of total number of transactions for C are 5,000. Thus Expected Confidence is 5,000/1,00,000=5%. For the supermarket example the Lift = Confidence/Expected Confidence = 40%/5% = 8. Hence, Lift is a value that gives us information about the increase in probability of the then (consequent) given the if (antecedent) part.

A lift ratio larger than 1.0 implies that the relationship between the antecedent and the consequent is more significant than would be expected if the two sets were independent. The larger the lift ratio, the more significant the association.