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Association Rules l Mining Association Rules between Sets of Items in Large Databases (R. Agrawal, T. Imielinski & A. Swami) 1993. l Fast Algorithms for Mining Association Rules (R. Agrawal & R. Srikant) 1994.

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Basket Data Retail organizations, e.g., supermarkets, collect and store massive amounts sales data, called basket data. A record consist of n transaction date n items bought Or, basket data may consist of items bought by a customer over a period.

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Example Association Rule 90% of transactions that purchase bread and butter also purchase milk Antecedent: bread and butter Consequent: milk Confidence factor: 90%

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Example Queries l Find all the rules that have Uluda Gazozu as consequent. l Find all rules that have Diet Coke in the antecedent. l Find all rules that have sausage in the antecedent and mustard in the consequent. l Find all the rules relating items located on shelves A and B in the store. l Find the best (most confident) k rules that have Uluda Gazozu in the consequent.

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Formal Model I = i 1, i 2, , i m : set of literals (items) D : database of transactions T D : a transaction. T I n TID: unique identifier, associated with each T X: a subset of I n T contains X if X T.

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Formal Model (Cont.) Association rule: X Y here X I, Y I and X Y = . Rule X Y has a confidence c in D if c% of transactions in D that contain X also contain Y. Rule X Y has a support s in D if s% of transactions in D contain X Y.

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Example I : itemset {cucumber, parsley, onion, tomato, salt, bread, olives, cheese, butter} D : set of transactions 1{{cucumber, parsley, onion, tomato, salt, bread}, 2 {tomato, cucumber, parsley}, 3 {tomato, cucumber, olives, onion, parsley}, 4 {tomato, cucumber, onion, bread}, 5 {tomato, salt, onion}, 6 {bread, cheese} 7 {tomato, cheese, cucumber} 8 {bread, butter}}