Transcription of Association Analysis: Basic Concepts and Algorithms
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Association Analysis: Basic Concepts and Algorithms
6 Association Analysis: Basic Concepts andAlgorithmsMany business enterprises accumulate large quantities of data from their day-to-day operations. For example, huge amounts of customer purchase data arecollected daily at the checkout counters of grocery stores. Table illustratesan example of such data, commonly known asmarket basket row in this table corresponds to a transaction, which contains a uniqueidentifier labeledTIDand a set of items bought by a given customer. Retail-ers are interested in analyzing the data to learn about the purchasing behaviorof their customers. Such valuable information can be used to support a vari-ety of business-related applications such as marketing promotions, inventorymanagement, and customer relationship chapter presents a methodology known asassociation analysis,which is useful for discovering interesting relationships hidden in large datasets. The uncovered relationships can be represented in the form ofassocia-Table example of market basket {Bread, Milk}2{Bread, Diapers, Beer, Eggs}3{Milk, Diapers, Beer, Cola}4{Bread, Milk, Diapers, Beer}5{Bread, Milk, Diapers, Cola}328 Chapter 6 Association Analysistion rulesor sets of frequent items.
tionships occurring over time (e.g., ozone depletion leads to global warming). Formulation of Association Rule Mining Problem The association rule mining problem can be formally stated as follows: Definition 6.1 (Association Rule Discovery). Given a set of transactions T, find all the rules having support ≥ minsup and confidence ≥ minconf,
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