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Chap11_AssociationRules - Chapter 11 Association Rules Data...

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Chapter 11 – Association Rules © Galit Shmueli and Peter Bruce 2008 Data Mining for Business Intelligence Shmueli, Patel & Bruce
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What are Association Rules? Study of “what goes with what” “Customers who bought X also bought Y” What symptoms go with what diagnosis Transaction-based or event-based Also called “market basket analysis” and “affinity analysis” Originated with study of customer transactions databases to determine associations among items purchased
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Used in many recommender systems
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Generating Rules
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Terms “IF” part = antecedent “THEN” part = consequent “Item set” = the items (e.g., products) comprising the antecedent or consequent Antecedent and consequent are disjoint (i.e., have no items in common)
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Tiny Example: Phone Faceplates
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Many Rules are Possible For example: Transaction 1 supports several rules, such as “If red, then white” (“If a red faceplate is purchased, then so is a white one”) “If white, then red” “If red and white, then green” + several more
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Frequent Item Sets Ideally, we want to create all possible combinations of items Problem: computation time grows exponentially as # items increases Solution: consider only “frequent item sets” Criterion for frequent: support
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