數(shù)據(jù)挖掘-Chapter5-購物籃分析與關聯(lián)規(guī)則

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訂單數(shù)常常是劃分客戶的有用方式好的客戶明顯比不好的客戶訂購頻率高較好的客戶傾向于購買更多樣性的產品零售商如何鼓勵顧客回頭購買更多不同的產品？SalesOrderCharacteristicsDidtheorderusegiftwrap?禮品包裝BillingaddresssameasShippingaddress?賬單/郵寄地址Didpurchaseraccept/declineacross-sell?交叉銷售Whatisthemostcommonitemfoundonaone-itemorder?Whatisthemostcommonitemfoundonamulti-itemorder?Whatisthemostcommonitemforrepeatcustomerpurchases?Howhasorderingofanitemchangedovertime?Howdoestheorderingofanitemvarygeographically?上述問題可以通過引入表示客戶訂單大小和數(shù)目的虛擬項以及代表時間和地域的虛擬項，結合關聯(lián)規(guī)則，提供相應的答案TrackingMarketingIntervention需要知道市場營銷干預的確切時間，確定額外的銷售是由干預引起的，還是由那些在晚些時候無論如何都將購買該產品的客戶做出的。觀察包含干預項的籃子的數(shù)目，如果客戶數(shù)目不是在增加，就有證據(jù)說明現(xiàn)有的客戶只不過是在該項上低價囤積打折是否導致了其他產品的額外銷售？通過發(fā)現(xiàn)促銷階段促銷產品的組合來解答PivotingforClusterAlgorithms通過轉軸，每行代表一個訂單，對購買的每件商品有標記或計數(shù)器除產品分層外，可用信息還包括服裝顏色、食品是否低卡路里、海報是否有背景等。這種描述能夠引出特別有用的問題：節(jié)食產品是否趨于一同銷售？

客戶在同一時間購買同一色系的服裝嗎？購買海報上產品的客戶是否也購買其他產品？AssociationRules購買芭比娃娃的沃爾瑪客戶中有60%的可能也購買三種類型的糖塊中的一種[Forbes,Sept8,1997]購買維修包養(yǎng)協(xié)議的客戶很可能購買大型家用電器(authorexperience)當一個新的五金店開張時，最常賣的商品之一是抽水馬桶清潔器(authorexperience)Sowhat…關聯(lián)規(guī)則容易理解，但它們并不總是有用的！AssociationRulesAssociationruletypes:ActionableRules可操作的規(guī)則–containhigh-quality,actionableinformation暗示芭比娃娃和糖果組合銷售TrivialRules平凡的規(guī)則–informationalreadywell-knownbythosefamiliarwiththebusiness買油漆的客戶也買油漆刷InexplicableRules費解的規(guī)則–noexplanationanddonotsuggestactionTrivialandInexplicableRulesoccurmostoftenTalesofBeers&Diapers故事發(fā)生在美國中西部的某地，一個零售商在分析銷售點數(shù)據(jù)時發(fā)現(xiàn)了啤酒和尿布之間的關聯(lián)。閃念之間，頭腦會做出如下解釋：喝啤酒的人不想中斷他們觀看體育賽事，因此他們購買了尿布以減少前往衛(wèi)生間的次數(shù)。零售商能夠利用這一信息做什么呢？A. 把啤酒和尿布緊靠著放在一起，于是當顧客購買了其中一個時，會記得去購買另外一個B. 把它們放置得盡可能遠，于是客戶必須走過盡可能多的貨架，從而有機會購買更多的產品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…一種特定的外套極可能與一種禮券一起購買，這是一條能夠用于改進禮券和外套二者售賣關系的信息CreatingAssociationRules1.選擇恰當?shù)捻椉?.通過判讀在同現(xiàn)矩陣中的計數(shù)產生規(guī)則3.克服數(shù)萬個項帶來的實際局限選擇恰當?shù)捻椉櫩捅人_餅牛奶糖蘋果咖啡1√2√√3√√√4√√5√√√√CreatingAssociationRules顧客額外的干奶酪洋蔥胡椒粉蘑菇橄欖油1√√√2√3√√√4√√5√√√√產品分層大份炸薯條和小份炸薯條是同一種產品嗎？冰淇淋的品牌比口味更具相關性嗎？服裝的尺寸、格調、款式和設計師哪個更重要？項數(shù)增加時，組合數(shù)也快速地增長；項越具體，結果的可操作性可能更高；折衷的辦法是一開始使用概括的項，然后反復生成規(guī)則，把目標放到更具體的項上。CreatingAssociationRules虛擬項使分析有能力利用超出產品分層的信息食品店中的低脂和無脂產品，家用電器上的節(jié)能選項，該交易使用的是現(xiàn)金、信用卡還是支票，該交易出現(xiàn)在星期幾或者一天中的什么時間只有在支持度和置信度都