If we think of the Universe as the set of items available at the store, then each item has boolean variable representing the presence or absence of that item. Each cart can then be represented by a Boolean vector of values assigned to these variables. The Boolean vectors can be analyzed for buying patterns that reflect items that are frequently associated or purchsed together.

Following is an example for Association Rule

Rule support and confidence are two measures of rule interestingness

1. They respectively reflect the usefulness and certainty of discovered rules
2. A support of 2% for Association Rule means that 2% of all the transactions under analysis show that computer and antivirus software are purchased together
3. A confidence of 60% means that 60% of the customers who purchased a computer also bought the software.

One more example for association rules

Typically, association rules are considered interesting if they satisfy both a minimum support threshold and a minimum confidence threshold. Such thresholds can be set by users or domain experts.

Additional analysis can be performed to uncover interesting statistical correlations between associated items.

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Reference: Data Mining Concepts and Techniques by Jiawei Han and Micheline Kambe

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