数据挖掘-Chapter5-购物篮分析与关联规则

Chapter5MarketBasketAnalysisandAssociationRulesMarketBasketAnalysisWhatcanbeinferred?IpurchasediapersIpurchaseanewcarIpurchaseOTCcoughmedicineIpurchaseaprescriptionmedicationIdon’tshowupforclassMarketBasketAnalysisRetail–eachcustomerpurchasesdifferentsetofproducts,differentquantities,differenttimesMBAusesthisinformationto:Identifywhocustomersare(notbyname)UnderstandwhytheymakecertainpurchasesGaininsightaboutitsmerchandise(products):FastandslowmoversProductswhicharepurchasedtogetherProductswhichmightbenefitfrompromotionTakeaction:StorelayoutsWhichproductstoputonspecials,promote,coupons…CombiningallofthiswithacustomerloyaltycarditbecomesevenmorevaluableAssociationRulesDataMiningtechniquemostcloselyalliedwithMarketBasketAnalysisAssociationRulescanbeautomaticallygeneratedARrepresentpatternsinthedatawithoutaspecifiedtargetvariableGoodexampleofundirecteddataminingWhetherpatternsmakesenseisuptohumanoids(us!)AssociationRulesApplyElsewhereBesidesretail–supermarkets,etc…Purchasesmadeusingcredit/debitcardsOptionalTelcoServicepurchasesBankingservicesUnusualcombinationsofinsuranceclaimscanbeawarningoffraudMedicalpatienthistoriesMarketBasketAnalysisDrill-DownMBAisasetoftechniques,AssociationRulesbeingmostcommon,thatfocusonpoint-of-sale(p-o-s)transactiondata3typesofmarketbasketdata(p-o-sdata)CustomersOrders(basicpurchasedata)Items(merchandise/servicespurchased)TypicalDataStructure(RelationalDatabase)LotsofquestionscanbeansweredAvg#oforders/customerAvg#uniqueitems/orderAvg#ofitems/orderForaproductWhat%ofcustomershavepurchasedAvg#orders/customerincludeitAvgquantityofitpurchased/orderEtc…Visualizationisextremelyhelpful…nextslideTransactionDataSalesOrderCharacteristics订单数常常是划分客户的有用方式好的客户明显比不好的客户订购频率高较好的客户倾向于购买更多样性的产品零售商如何鼓励顾客回头购买更多不同的产品？SalesOrderCharacteristicsDidtheorderusegiftwrap?礼品包装BillingaddresssameasShippingaddress?账单/邮寄地址Didpurchaseraccept/declineacross-sell?交叉销售Whatisthemostcommonitemfoundonaone-itemorder?Whatisthemostcommonitemfoundonamulti-itemorder?Whatisthemostcommonitemforrepeatcustomerpurchases?Howhasorderingofanitemchangedovertime?Howdoestheorderingofanitemvarygeographically?上述问题可以通过引入表示客户订单大小和数目的虚拟项以及代表时间和地域的虚拟项，结合关联规则，提供相应的答案TrackingMarketingIntervention需要知道市场营销干预的确切时间，确定额外的销售是由干预引起的，还是由那些在晚些时候无论如何都将购买该产品的客户做出的。观察包含干预项的篮子的数目，如果客户数目不是在增加，就有证据说明现有的客户只不过是在该项上低价囤积打折是否导致了其他产品的额外销售？通过发现促销阶段促销产品的组合来解答PivotingforClusterAlgorithms通过转轴，每行代表一个订单，对购买的每件商品有标记或计数器除产品分层外，可用信息还包括服装颜色、食品是否低卡路里、海报是否有背景等。这种描述能够引出特别有用的问题：节食产品是否趋于一同销售？

客户在同一时间购买同一色系的服装吗？购买海报上产品的客户是否也购买其他产品？AssociationRules购买芭比娃娃的沃尔玛客户中有60%的可能也购买三种类型的糖块中的一种[Forbes,Sept8,1997]购买维修包养协议的客户很可能购买大型家用电器(authorexperience)当一个新的五金店开张时，最常卖的商品之一是抽水马桶清洁器(authorexperience)Sowhat…关联规则容易理解，但它们并不总是有用的！AssociationRulesAssociationruletypes:ActionableRules可操作的规则–containhigh-quality,actionableinformation暗示芭比娃娃和糖果组合销售TrivialRules平凡的规则–informationalreadywell-knownbythosefamiliarwiththebusiness买油漆的客户也买油漆刷InexplicableRules费解的规则–noexplanationanddonotsuggestactionTrivialandInexplicableRulesoccurmostoftenTalesofBeers&Diapers故事发生在美国中西部的某地，一个零售商在分析销售点数据时发现了啤酒和尿布之间的关联。闪念之间，头脑会做出如下解释：喝啤酒的人不想中断他们观看体育赛事，因此他们购买了尿布以减少前往卫生间的次数。零售商能够利用这一信息做什么呢？A. 把啤酒和尿布紧靠着放在一起，于是当顾客购买了其中一个时，会记得去购买另外一个B. 把它们放置得尽可能远，于是客户必须走过尽可能多的货架，从而有机会购买更多的产品HowGoodisanAssociationRule?CustomerItemsPurchased1OJ,soda2Milk,OJ,windowcleaner3OJ,detergent4OJ,detergent,soda5Windowcleaner,sodaOJWindowcleanerMilkSodaDetergentOJ41122Windowcleaner12110Milk11100Soda21031Detergent20012POSTransactionsCo-occurrenceofProductsHowGoodisanAssociationRule?OJWindowcleanerMilkSodaDetergentOJ41122Windowcleaner12110Milk11100Soda21031Detergent20012Simplepatterns:1.OJandsodaaremorelikelypurchasedtogetherthananyothertwoitems2.Detergentisneverpurchasedwithmilkorwindowcleaner3.MilkisneverpurchasedwithsodaordetergentHowGoodisanAssociationRule?Whatistheconfidenceforthisrule:Ifacustomerpurchasessoda,thencustomeralsopurchasesOJ2outof3sodapurchasesalsoincludeOJ,so67%Whatabouttheconfidenceofthisrulereversed?2outof4OJpurchasesalsoincludesoda,so50%Confidence=Ratioofthenumberoftransactionswithalltheitemstothenumberoftransactionswithjustthe“if”itemsCustomerItemsPurchased1OJ,soda2Milk,OJ,windowcleaner3OJ,detergent4OJ,detergent,soda5Windowcleaner,sodaPOSTransactionsHowGoodisanAssociationRule?Howmuchbetterthanchanceisarule?Lift(improvement)tellsushowmuchbetteraruleisatpredictingtheresultthanjustassumingtheresultinthefirstplaceLiftistheratiooftherecordsthatsupporttheentireruletothenumberthatwouldbeexpected,assumingtherewasnorelationshipbetweentheproductsCalculatinglift.Whenlift>1thentheruleisbetteratpredictingtheresultthanguessingWhenlift<1,theruleisdoingworsethaninformedguessingandusingtheNegativeRuleproducesabetterrulethanguessingCo-occurrencecanoccurin3,4,ormoredimensions…一种特定的外套极可能与一种礼券一起购买，这是一条能够用于改进礼券和外套二者售卖关系的信息CreatingAssociationRules1.选择恰当的项集2.通过判读在同现矩阵中的计数产生规则3.克服数万个项带来的实际局限选择恰当的项集顾客比萨饼牛奶糖苹果咖啡1√2√√3√√√4√√5√√√√CreatingAssociationRules顾客额外的干奶酪洋葱胡椒粉蘑菇橄榄油1√√√2√3√√√4√√5√√√√产品分层大份炸薯条和小份炸薯条是同一种产品吗？冰淇淋的品牌比口味更具相关性吗？服装的尺寸、格调、款式和设计师哪个更重要？项数增加时，组合数也快速地增长；项越具体，结果的可操作性可能更高；折衷的办法是一开始使用概括的项，然后反复生成规则，把目标放到更具体的项上。CreatingAssociationRules虚拟项使分析有能力利用超出产品分层的信息食品店中的低脂和无脂产品，家用电器上的节能选项，该交易使用的是现金、信用卡还是支票，该交易出现在星期几或者一天中的什么时间只有在支持度和置信度都