stanford大学-大数据挖掘-introduction.ppt

1,CS345A:DataMiningontheWeb,CourseIntroductionIssuesinDataMiningBonferronisPrinciple,2,CourseStaff,Instructors:AnandRajaramanJeffUllmanR.M/class/cs345a.,3,Requirements,Homework(Gradianceandother)20%G,4,Project,Softwareimplementationrelatedtocoursesubjectmatter.Shouldinvolveanoriginalcomponentorexperiment.Morelateraboutavailabledataandcomputingresources.,5,PossibleProjects,Manypastprojectshavedealtwithcollaborativefiltering(advicebasedonwhatsimilarpeopledo).E.g.,NetflixChallenge.Othershavedealtwithengineeringsolutionsto“machine-learning”problems.,6,ML-ReplacementProjects,MLgenerallyrequiresalarge“trainingset”ofcorrectlyclassifieddata.Example:classifyingWebpagesbytopic.Hardtofindwell-classifieddata.Exception:OpenDirectoryworksforpagetopics,becauseworkiscollaborativeandsharedbymany.Othergoodexceptions?,7,ML-Replacement(2),ManyproblemsrequirethoughtratherthanML:Tellimportantpagesfromunimportant(PageRank).Tellrealnewsfrompublicity(how?).Distinguishpositivefromnegativeproductreviews(how?).Etc.,etc.,8,TeamProjects,WorkinginpairsOK,butNomorethantwoperproject.Wewillexpectmorefromapairthanfromanindividual.Theeffortshouldberoughlyevenlydistributed.,9,WhatisDataMining?,Discoveryofuseful,possiblyunexpected,patternsindata.Subsidiaryissues:Datacleaning:detectionofbogusdata.E.g.,age=150.Entityresolution.Visualization:somethingbetterthanmegabytefilesofoutput.,10,Cultures,Databases:concentrateonlarge-scale(non-main-memory)data.AI(machine-learning):concentrateoncomplexmethods,smalldata.Statistics:concentrateonmodels.,11,Modelsvs.AnalyticProcessing,Toadatabaseperson,data-miningisanextremeformofanalyticprocessingqueriesthatexaminelargeamountsofdata.Resultisthequeryanswer.Toastatistician,data-miningistheinferenceofmodels.Resultistheparametersofthemodel.,12,(WaytooSimple)Example,Givenabillionnumbers,aDBpersonwouldcomputetheiraverageandstandarddeviation.AstatisticianmightfitthebillionpointstothebestGaussiandistributionandreportthemeanandstandarddeviationofthatdistribution.,13,OutlineofCourse,Map-ReduceandHadoop.Associationrules,frequentitemsets.PageRankandrelatedmeasuresofimportanceontheWeb(linkanalysis).Spamdetection.Topic-specificsearch.Recommendationsystems.Collaborativefiltering.,14,Outline(2),Findingsimilarsets.Minhashing,Locality-Sensitivehashing.Extractingstructureddata(relations)fromtheWeb.Clusteringdata.ManagingWebadvertisements.Miningdatastreams.,15,MeaningfulnessofAnswers,Abigdata-miningriskisthatyouwill“discover”patternsthataremeaningless.StatisticianscallitBonferronisprinciple:(roughly)ifyoulookinmoreplacesforinterestingpatternsthanyouramountofdatawillsupport,youareboundtofindcrap.,16,ExamplesofBonferronisPrinciple,AbigobjectiontoTIAwasthatitwaslookingforsomanyvagueconnectionsthatitwassuretofindthingsthatwerebogusandthusviolateinnocentsprivacy.TheRhineParadox:agreatexampleofhownottoconductscientificresearch.,17,StanfordProfessorProvesTrackingTerroristsIsImpossible!,Threeyearsago,theexampleIamabouttogiveyouwaspickedupfrommyclassslidesbyareporterfromtheLATimes.Despitemytalkingtohimatlength,hewasunabletograspthepointthatthestorywasmadeuptoillustrateBonferronisPrinciple,andwasnotreal.,18,The“TIA”Story,Supposewebelievethatcertaingroupsofevil-doersaremeetingoccasionallyinhotelstoplotdoingevil.Wewanttofind(unrelated)peoplewhoatleasttwicehavestayedatthesamehotelonthesameday.,19,TheDetails,109peoplebeingtracked.1000days.Eachpersonstaysinahotel1%ofthetime(10daysoutof1000).Hotelshold100people(so105hotels).Ifeveryonebehavesrandomly(I.e.,noevil-doers)willthedataminingdetectanythingsuspicious?,20,Calculations(1),Probabilitythatgivenpersonspandqwillbeatthesamehotelongivendayd:1/1001/10010-5=10-9.Probabilitythatpandqwillbeatthesamehotelongivendaysd1andd2:10-910-9=10-18.Pairsofdays:5105.,21,Calculations(2),Probabilitythatpandqwillbeatthesamehotelonsometwodays:510510-18=510-13.Pairsofpeople:51017.Expectednumberof“suspicious”pairsofpeople:51017510-13=250,000.,22,Conclusion,Supposethereare(say)10pairsofevil-doerswhodefinitelystayedatthesamehoteltwice.Analystshavetosiftthrough250,010candidatestofindthe10realcases.Notgonnahappen.Buthowcanweimprovethescheme?,23,Moral,Whenlookingforaproperty(e.g.,“twopeoplestayedatthesamehoteltwice”),makesurethatthepropertydoesnotallowsomanypossibilitiesthatrandomdatawillsurelyproducefacts“ofinterest.”,24,RhineParadox(1),JosephRhinewasaparapsychologistinthe1950swhohypothesizedthatsomepeoplehadExtra-SensoryPerception.Hedevised(somethinglike)anexperimentwheresubjectswereaskedtoguess10hiddencardsredorblue.Hediscoveredthatalmost1in1000hadESPtheywereabletogetall10right!,25,RhineParadox(2),HetoldthesepeopletheyhadESPandcalledtheminforanothertestofthesametype.Alas,hediscoveredthatalmos