a7pru01Intro.ppt

1,1,Data Mining:Concepts and Techniques(3rd ed.)Chapter 1,Jiawei Han,Micheline Kamber,and Jian PeiUniversity of Illinois at Urbana-Champaign&Simon Fraser University2012 Han,Kamber&Pei.All rights reserved.,May 7,2024,Data Mining:Concepts and Techniques,2,May 7,2024,Data Mining:Concepts and Techniques,2,3,3,Data and Information Systems(DAIS:)Course Structures at CS/UIUC,Coverage:Database,data mining,text information systems,Web and bioinformaticsData miningIntro.to data warehousing and mining(CS412:HanFall)Data mining:Principles and algorithms(CS512:HanSpring)Seminar:Advanced Topics in Data mining(CS591HanFall and Spring.1 credit unit)Independent Study:only if you seriously plan to do your Ph.D./M.S.on data mining and try to demonstrate your abilityDatabase Systems:Introd.to database systems(CS411:Kevin Chang+Saurabh Sinha:Spring and Fall)Advanced database systems(CS511:Kevin Chang Fall11)Text information systemsText information system(CS410 ChengXiang Zhai:Spring)BioinformaticsIntroduction to BioInformatics(Saurabh Sinha)CS591 Seminar on Bioinformatics(Sinha:1 credit unit)Yahoo!-DAIS seminar(CS591DAISFall and Spring.1 credit unit),4,4,CS412 Coverage(Chapters 1-10,3rd Ed.),CS412 Coverage(BK2:2nd Ed.)IntroductionData PreprocessingData Warehouse and OLAP Technology:An IntroductionAdvanced Data Cube Technology and Data GeneralizationMining Frequent Patterns,Association and CorrelationsClassification and PredictionCluster Analysis,CS412 Coverage(BK3:3rd ed.)IntroductionGetting to Know Your DataData PreprocessingData Warehouse and OLAP Technology:An IntroductionAdvanced Data Cube Technology Mining Frequent Patterns&Association:Basic ConceptsMining Frequent Patterns&Association:Advanced MethodsClassification:Basic Concepts Classification:Advanced MethodsCluster Analysis:Basic ConceptsCluster Analysis:Advanced Methods(CS512)Outlier Analysis(CS512),The textbook book will be covered in two courses at CS,UIUCCS412:Introduction to Data Mining(Fall)Chapters 1-10CS512:Data Mining:Principles and Algorithms(Spring)Chaps.11-13,5,5,CS512 Coverage(Chapters 11,12,13+More Advanced Topics),Cluster Analysis:Advanced Methods(Chapter 11)Outlier Analysis(Chapter 12)Mining data streams,time-series,and sequence dataMining graph dataMining social and information networksMining object,spatial,multimedia,text and Web dataMining complex data objectsSpatial and spatiotemporal data miningMultimedia data miningText and Web miningAdditional(often current)themes if time permits,6,6,7,7,CS 412.Course Page&Class Schedule,Class Homepage:https:/wiki.engr.illinois.edu/display/cs412Wiki course outlineCourse InformationCourse Schedule(Fall_2011)Lecture media(Fall_2011)Assignments(Fall 2011)Resources and Reading ListsStaffProject Only for students taking 4 credits for the courseComments and SuggestionsTextbook,Slides,Class Presentation,and TeachingClass-Related Questions and Answers,8,8,CS 412:Course Project 4th credit,A comprehensive survey on a focused topicIndividual surveys,not group workExamples of topics(need to be focused and specific)Data cleaning for effective social network analysisClustering data streams in big dataTransfer learning for recommender systemsMining compressed frequent patternsCounter examples of topics(too general or too specific)Scalable classification methods Too generalThe FPGrowth algorithm for mining frequent patterns Too specificNote:No copy of textbook sections!No plagiarism!,9,Chapter 1.Introduction,Why Data Mining?What Is Data Mining?A Multi-Dimensional View of Data MiningWhat Kinds of Data Can Be Mined?What Kinds of Patterns Can Be Mined?What Kinds of Technologies Are Used?What Kinds of Applications Are Targeted?Major Issues in Data MiningA Brief History of Data Mining and Data Mining SocietySummary,10,Why Data Mining?,The Explosive Growth of Data:from terabytes to petabytesData collection and data availabilityAutomated data collection tools,database systems,Web,computerized societyMajor sources of abundant dataBusiness:Web,e-commerce,transactions,stocks,Science:Remote sensing,bioinformatics,scientific simulation,Society and everyone:news,digital cameras,YouTube We are drowning in data,but starving for knowledge!“Necessity is the mother of invention”Data miningAutomated analysis of massive data sets,11,Evolution of Sciences:New Data Science Era,Before 1600:Empirical science1600-1950s:Theoretical scienceEach discipline has grown a theoretical component.Theoretical models often motivate experiments and generalize our understanding.1950s-1990s:Computational scienceOver the last 50 years,most disciplines have grown a third,computational branch(e.g.empirical,theoretical,and computational ecology,or physics,or linguistics.)Computational Science traditionally meant simulation.It grew out of our inability to find closed-form solutions for complex mathematical models.1990-now:Data scienceThe flood of data from new scientific instruments and simulationsThe ability to economically store and manage petabytes of data onlineThe Internet and computing Grid that makes all these archives universally accessible Scientific info.management,acquisition,organization,query,and visualization tasks scale almost linearly with data volumesData mining is a major new challenge!Jim Gray and Alex Szalay,The World Wide Telescope:An Archetype for Online Science,Comm.ACM,45(11):50-54,Nov.2002,12,Chapter 1.Introduction,Why Data Mining?What Is Data Mining?A Multi-Dimensional View of Data MiningWhat Kinds of Data Can Be Mined?What Kinds of Patterns Can Be Mined?What Kinds of Technologies Are Used?What Kinds of Applications Are Targeted?Major Issues in Data MiningA Brief History of Data Mining and Data Mining SocietySummary,13,What Is Data Mining?,Data mining(knowledge discovery from data)Extraction of interesting(non-trivial,implicit,previously unknown and potentially useful)patterns or knowledge from huge amount of dataData mining:a misnomer?Alternative namesKnowledge discovery(mining)in databases(KDD),knowledge extraction,data/pattern analysis,data archeology,data dredging,information harvesting,business intelligence,etc.Watch out:Is everything“data mining”?Simple search and query processing(Deductive)expert systems,14,Knowledge Discovery(KDD)Process,This is a view from typical database systems and data warehousing communitiesData mining plays an essential role in the knowledge discovery process,Data Cleaning,Data Integration,Databases,Data Warehouse,Knowledge,Task-relevant Data,Selection,Data Mining,Pattern Evaluation,15,Example:A Web Mining Framework,Web mining usually involvesData cleaningData integration from multiple sourcesWarehousing the dataData cube constructionData selection for data miningData miningPresentation of the mining resultsPatterns and knowledge to be used or stored into knowledge-base,16,Data Mining in Business Intelligence,Increasing potentialto supportbusiness decisions,End User,Business Analyst,DataAnalyst,DBA,Decision Making,Data Presentation,Visualization Techniques,Data Mining,Information Discovery,Data Exploration,Statistical Summary,Querying,and Reporting,Data Preprocessing/Integration,Data Warehouses,Data Sources,Paper,Files,Web documents,Scientific experiments,Database Systems,17,KDD Process:A Typical View from ML and Statistics,Input Data,PatternInformationKnowledge,Data Mining,Data Pre-Processing,Post-Processing,This is a view from typical machine learning and statistics communities,Pattern discoveryAssociation&correlationClassificationClusteringOutlier analysis,18,Which View Do You Prefer?,Which view do you prefer?KDD vs.ML/Stat.vs.Business IntelligenceDepending on the data,applications,and your focusData Mining vs.Data ExplorationBusiness intelligence viewWarehouse,data cube,reporting but not much miningBusiness objects vs.data mining toolsSupply chain example:mining vs.OLAP vs.presentation toolsData presentation vs.data exploration,19,Chapter 1.Introduction,Why Data Mining?What Is Data Mining?A Multi-Dimensional View of Data MiningWhat Kinds of Data Can Be Mined?What Kinds of Patterns Can Be Mined?What Kinds of Technologies Are Used?What Kinds of Applications Are Targeted?Major Issues in Data MiningA Brief History of Data Mining and Data Mining SocietySummary,20,Multi-Dimensional View of Data Mining,Data to be minedDatabase data(extended-relational,object-oriented,heterogeneous,legacy),data warehouse,transactional data,stream,spatiotemporal,time-series,sequence,text and web,multi-media,graphs&social and information networksKnowledge to be mined(or:Data mining functions)Characterization,discrimination,association,classification,clustering,trend/deviation,outlier analysis,etc.Descriptive vs.predictive data mining Multiple/integrated functions and mining at multiple levelsTechniques utilizedData-intensive,data warehouse(OLAP),machine learning,statistics,pattern recognition,visualization,high-performance,etc.Applications adaptedRetail,telecommunication,banking,fraud analysis,bio-data mining,stock market analysis,text mining,Web mining,etc.,21,Chapter 1.Introduction,Why Data Mining?What Is Data Mining?A Multi-Dimensional View of Data MiningWhat Kinds of Data Can Be Mined?What Kinds of Patterns Can Be Mined?What Kinds of Technologies Are Used?What Kinds of Applications Are Targeted?Major Issues in Data MiningA Brief History of Data Mining and Data Mining SocietySummary,22,Data Mining:On What Kinds of Data?,Database-oriented data sets and applicationsRelational database,data warehouse,transactional databaseAdvanced data sets and advanced applications Data streams and sensor dataTime-series data,temporal data,sequence data(incl.bio-sequences)Structure data,graphs,social networks and multi-linked dataObject-relational databasesHeterogeneous databases and legacy databasesSpatial data and spatiotemporal dataMultimedia databaseText databasesThe World-Wide Web,23,Chapter 1.Introduction,Why Data Mining?What Is Data Mining?A Multi-Dimensional View of Data MiningWhat Kinds of Data Can Be Mined?What Kinds of Patterns Can Be Mined?What Kinds of Technologies Are Used?What Kinds of Applications Are Targeted?Major Issues in Data MiningA Brief History of Data Mining and Data Mining SocietySummary,24,Data Mining Function:(1)Generalization,Information integration and data warehouse constructionData cleaning,transformation,integration,and multidimensional data modelData cube technologyScalable methods for computing(i.e.,materializing)multidimensional aggregatesOLAP(online analytical processing)Multidimensional concept description:Characterization and discriminationGeneralize,summarize,and contrast data characteristics,e.g.,dry vs.wet region,25,Data Mining Function:(2)Association and Correlation Analysis,Frequent patterns(or frequent itemsets)What items are frequently purchased together in your Walmart?Association,correlation vs.causalityA typical association ruleDiaper Beer 0.5%,75%(support,confidence)Are strongly associated items also strongly correlated?How to mine such patterns and rules efficiently in large datasets?How to use such patterns for classification,clustering,and other applications?,26,Data Mining Function:(3)Classification,Classification and label prediction Construct models(functions)based on some training examplesDescribe and distinguish classes or concepts for future predictionE.g.,classify countries based on(climate),or classify cars based on(gas mileage)Predict some unknown class labelsTypical methodsDecision trees,nave Bayesian classification,support vector machines,neural networks,rule-based classification,pattern-based classification,logistic regression,Typical applications:Credit card fraud detection,direct marketing,classifying stars,diseases,web-pages,27,Data Mining Function:(4)Cluster Analysis,Unsupervised learning(i.e.,Class label is unknown)Group data to form new categories(i.e.,clusters),e.g.,cluster houses to find distribution patternsPrinciple:Maximizing intra-class similarity&minimizing interclass similarityMany methods and applications,28,Data Mining Function:(5)Outlier Analysis,Outlier analysisOutlier:A data object that does not comply with the general behavior of the dataNoise or exception?One persons garbage could be another persons treasureMethods:by product of clustering or regression analysis,Useful in fraud detection,rare events analysis,29,Time and Ordering:Sequential Pattern,Trend and Evolution Analysis,Sequence,trend and evolution analysisTrend,time-series,and deviation analysis:e.g.,regression and value predictionSequential pattern mininge.g.,first buy digital camera,then buy large SD memory cardsPeriodicity analysisMotifs and biological sequence analysisApproximate and consecutive motifsSimilarity-based analysisMining data streamsOrdered,time-varying,potentially infinite,data streams,30,Structure and Network Analysis,Graph miningFinding frequent subgraphs(e.g.,chemical compounds),trees(XML),substructures(web fragments)Information network analysisSocial networks:actors(objects,nodes)and relationships(edges)e.g.,author networks in CS,terrorist networksMultiple heterogeneous networksA person could be multiple information networks:friends,family,classmates,Links carry a lot of semantic information:Link miningWeb miningWeb is a big information network:from PageRank to GoogleAnalysis of Web information networksWeb community discovery,opinion mining,usage mining,31,Evaluation of Knowledge,Are all mined knowledge interesting?One can mine tremendous amount of“patterns”and knowledgeSome may fit only certain dimension space(time,location,)Some may not be representative,may be transient,Evaluation of mined knowledge directly mine only interesting knowledge?Descriptive vs.predictiveCoverageTypicality vs.noveltyAccuracyTimeliness,32,Chapter 1.Introduction,Why Data Mining?What Is Data Mining?A Multi-Dimensional View of Data MiningWhat Kinds of Data Can Be Mined?What Kinds of Patterns Can Be Mined?What Kinds of Technologies Are Used?What Kinds of Applications Are Targeted?Major Issues in Data MiningA Brief History of Data Mining and Data Mining SocietySummary,33,Data Mining:Confluence of Multiple Disciplines,Data Mining,MachineLearning,Statistics,Applications,Algorithm,PatternRecognition,High-PerformanceComputing,Visualization,Database Technology,34,Why Confluence of Multiple Disciplines?,Tremendous amount of dataAlgorithms must be highly scalable to handle such as tera-bytes of dataHigh-dimensionality of data Micro-array may have tens of thousands of dimensionsHigh complexity of dataData streams and sensor dataTime-series data,temporal data,sequence data Structure data,graphs,social networks and multi-linked dataHeterogeneous databases and legacy databasesSpatial,spatiotemporal,multimedia,text and Web dataSoftware programs,scientific simulationsNew and sophisticated applications,35,Chapter 1.Introduction,Why Data Mining?What Is Data Mining?A Multi-Dimensional View of Data MiningWhat Kinds of Data Can Be Mined?What Kinds of Patterns Can Be Mined?What Kinds of Technologies Are Used?What Kinds of Applications Are Ta