Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Association Rule Mining Given a set of transactions, find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction Market-Basket transactions TID Items 1 Bread, Milk 2 3 4 5 Bread, Diaper, Beer, Eggs Milk, Diaper, Beer, Coke Bread, Milk, Diaper, Beer Bread, Milk, Diaper, Coke © Tan,Steinbach, Kumar Introduction to Data Mining Example of Association Rules {Diaper} → {Beer}, {Milk, Bread} → {Eggs,Coke}, {Beer, Bread} → {Milk}, Implication means co-occurrence, not causality! 4/18/2004 ‹#› Definition: Frequent Itemset Itemset A collection of one or more items ◆ Example: {Milk, Bread, Diaper} k-itemset ◆ An itemset that contains k items Support count () Frequency of occurrence of an itemset E.g. ({Milk, Bread,Diaper}) = 2 Support TID Items 1 Bread, Milk 2 3 4 5 Bread, Diaper, Beer, Eggs Milk, Diaper, Beer, Coke Bread, Milk, Diaper, Beer Bread, Milk, Diaper, Coke Fraction of transactions that contain an itemset E.g. s({Milk, Bread, Diaper}) = 2/5 Frequent Itemset An itemset whose support is greater than or equal to a minsup threshold © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#› Definition: Association Rule Association Rule An implication expression of the form X → Y, where X and Y are itemsets Example: {Milk, Diaper} → {Beer} Rule Evaluation Metrics TID Items 1 Bread, Milk 2 3 4 5 Bread, Diaper, Beer, Eggs Milk, Diaper, Beer, Coke Bread, Milk, Diaper, Beer Bread, Milk, Diaper, Coke Support (s) Example: ◆ Fraction of transactions that contain {Milk , Diaper }  Beer both X and Y Confidence (c) ◆ Measures how often items in Y appear in transactions that contain X s= c= © Tan,Steinbach, Kumar Introduction to Data Mining  (M

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