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CS 412 Intro. to Data MiningChapter 1. IntroductionJiawei Han, Computer Science, Univ. Illinois at Urbana -Champaign, 20171

August 28, 2017August 28, 2017Data Mining: Concepts and TechniquesData Mining: Concepts and Techniques22

Data and Information Systems (DAIS) Database SystemsKevin Chang Data MiningAdityaParameswaranJiawei Han Text Information Systems Networks3HariSundaramChengXiangZhai

Data and Information Systems(DAIS:) Course Structures at CS/UIUCCoverage: Database, data mining, text information systems, Web andbioinformatics Data mining Intro. to data warehousing and mining (CS412) Data mining: Principles and algorithms (CS512) Database Systems: Intro. to database systems (CS411) Advanced database systems (CS511) Text information systems Text information system (CS410) Advanced text information systems (CS510) 4

CS 412. Course Page & Class ScheduleTextbook Jiawei Han, Micheline Kamber and Jian Pei, DataMining: Concepts and Techniques (3rd ed),Morgan Kaufmann, 2011 Class 412 Bookmark on course schedule page Class Schedule: 9:30-10:45 am Tues./[email protected] Office hours: 10:45-11:30am Tues./Thurs. @2132SC Lecture media: recorded; but class attendance iscritical 5

CS 412. Fall 2017. Teach AssistantsDongming LeiCarl YangYu ShiChao ZhangShi Zhi(Online Session)TA office hours: 4-5pm (Mon.), 11-12pm (Wed.)@0207SC. Additional hours beforedue date will be announced at Piazza Wait list (No wait list at this time, keep attending class, see if there is spaceavailable or there is overflow section opening) If you cannot register but still desperately want to get in, please sign on whenthere is “potential opening”: Explain why you have to take the course This Fall! 6

CS 412. Course Work and GradingAssignments, Programming Assignments, and Exams Written Assignments: 15% (three homework assignments expected) Programming assignments: 20% (two programming assignments expected) Midterm exam: 30% Final exam: 35% For students taking 4th credit (TA will provide concrete instructions on the 4thcredit project) For students registering 4 credits: 25%. The overall scores will be scaledproportionally Need help and/or discussions? Sign on: Piazza (https://piazza.com/illinois/cs412) Check your homework/exam scores: Compass 7

Help Needed: LifeNet—A Structured Network-BasedKnowledge Exploration and Analytics System for Life Sciences What we are doing?A scalable system that transforms biomedical papers into aknowledge graph & supports various search/analytics functionsCCR4MDC1 8What we already have?A working prototype system & an ACL demo paperWhat we are looking for?Students with expertise on HTML/CSS & JavaScriptExperiences on web frameworks and databasesSystem design experience will be a big plusWhat you will gain?Hourly pay ( 12- 15 per hour, 6-20 hours per week)Possible research publications & a good thesis topicSend us your resume if interested: Jiaming Shen ([email protected])AspirinMay treatType PathMay pe PathDiseaseRAP2A KawasakiDiseaseNeoplasmsBreast NeoplasmsBRCA1May treatMay treatGeneMay treatBRAFDrugTargetTafinlarCBDCACDDPDisease

Chapter 1. Introduction9 Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What 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 Mining A Brief History of Data Mining and Data Mining Society Summary

Why Data Mining?The Explosive Growth of Data: from terabytes to petabytes Data collection and data availability Major sources of abundant data 10Automated data collection tools, database systems, Web, computerized society Business: 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 mining—Automated analysis ofmassive data sets

Chapter 1. Introduction11 Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What 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 Mining A Brief History of Data Mining and Data Mining Society Summary

What Is Data Mining? Extraction of interesting (non-trivial, implicit, previously unknown andpotentially useful) patterns or knowledge from huge amount of data Data mining: a misnomer? 12Data mining (knowledge discovery from data)Alternative namesKnowledge discovery (mining) in databases (KDD), knowledge extraction,data/pattern analysis, data archeology, data dredging, informationharvesting, business intelligence, etc.Watch out: Is everything “data mining”? Simple search and query processing (Deductive) expert systems

Knowledge Discovery (KDD) Process This is a view from typical database systemsand data warehousing communitiesData mining plays an essential role in theknowledge discovery processData MiningTask-relevant DataData WarehouseData CleaningData Integration13DatabasesPattern EvaluationSelection

Example: A Web Mining Framework 14Web mining usually involves Data cleaning Data integration from multiple sources Warehousing the data Data cube construction Data selection for data mining Data mining Presentation of the mining results Patterns and knowledge to be used or stored into knowledge-base

Data Mining in Business IntelligenceIncreasing potentialto supportbusiness decisionsDecisionMakingData PresentationVisualization TechniquesEnd UserBusinessAnalystData MiningInformation DiscoveryDataAnalystData ExplorationStatistical Summary, Querying, and ReportingData Preprocessing/Integration, Data WarehousesData SourcesPaper, Files, Web documents, Scientific experiments, Database Systems15DBA

KDD Process: A View from ML and StatisticsInput DataData PreProcessingData integrationNormalizationFeature selectionDimension reduction 16DataMiningPattern discoveryClassificationClusteringOutlier analysis PostProcessingPattern evaluationPattern selectionPattern interpretationPattern visualizationThis is a view from typical machine learning and statistics communities

Data Mining vs. Data ExplorationWhich view do you prefer? KDD vs. ML/Stat. vs. Business Intelligence Depending on the data, applications, and your focusData Mining vs. Data Exploration 17Business intelligence viewWarehouse, data cube, reporting but not much mining Business objects vs. data mining tools Supply chain example: mining vs. OLAP vs. presentation tools Data presentation vs. data exploration

Chapter 1. Introduction18 Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What 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 Mining A Brief History of Data Mining and Data Mining Society Summary

Multi-Dimensional View of Data MiningData to be mined Database data (extended-relational, object-oriented, heterogeneous), data warehouse,transactional data, stream, spatiotemporal, time-series, sequence, text and web, multimedia, graphs & social and information networks Knowledge to be mined (or: Data mining functions) Characterization, discrimination, association, classification, clustering, trend/deviation,outlier analysis, Descriptive vs. predictive data mining Multiple/integrated functions and mining at multiple levels Techniques utilized Data-intensive, data warehouse (OLAP), machine learning, statistics, pattern recognition,visualization, high-performance, etc. Applications adapted Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis,text mining, Web mining, etc. 19

Chapter 1. Introduction20 Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What 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 Mining A Brief History of Data Mining and Data Mining Society Summary

Data Mining: On What Kinds of Data? Relational database, data warehouse, transactional database Object-relational databases, Heterogeneous databases and legacy databases 21Database-oriented data sets and applicationsAdvanced data sets and advanced applications Data streams and sensor data Time-series data, temporal data, sequence data (incl. bio-sequences) Structure data, graphs, social networks and information networks Spatial data and spatiotemporal data Multimedia database Text databases The World-Wide Web

Chapter 1. Introduction22 Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What 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 Mining A Brief History of Data Mining and Data Mining Society Summary

Data Mining Functions: (1) Generalization Data cleaning, transformation, integration, andmultidimensional data modelData cube technology Scalable methods for computing (i.e., materializing)multidimensional aggregates OLAP (online analytical processing) 23Information integration and data warehouse constructionMultidimensional concept description: Characterizationand discriminationGeneralize, summarize, and contrast datacharacteristics, e.g., dry vs. wet region

Data Mining Functions: (2) Pattern DiscoveryFrequent patterns (or frequent itemsets) What items are frequently purchased together in your Walmart? Association and Correlation Analysis A typical association rule Diaper 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? 24

Data Mining Functions: (3) ClassificationClassification and label prediction Construct models (functions) based on some training examples Describe and distinguish classes or concepts for future prediction Ex. 1. Classify countries based on (climate) Ex. 2. Classify cars based on (gas mileage) Predict some unknown class labels Typical methods Decision trees, naïve Bayesian classification, support vector machines, neuralnetworks, rule-based classification, pattern-based classification, logisticregression, Typical applications: Credit card fraud detection, direct marketing, classifying stars, diseases, webpages, 25

Data Mining Functions: (4) Cluster Analysis26 Unsupervised learning (i.e., Class label isunknown) Group data to form new categories (i.e.,clusters), e.g., cluster houses to finddistribution patterns Principle: Maximizing intra-class similarity& minimizing interclass similarity Many methods and applications

Data Mining Functions: (5) Outlier Analysis 27Outlier analysis Outlier: A data object that does not comply with thegeneral behavior of the data Noise or exception?―One person’s garbage could beanother person’s treasure Methods: by product of clustering or regression analysis, Useful in fraud detection, rare events analysis

Data Mining Functions: (6) Time and Ordering:Sequential Pattern, Trend and Evolution AnalysisSequence, trend and evolution analysis Trend, time-series, and deviation analysis e.g., regression and value prediction Sequential pattern mining e.g., buy digital camera, then buy large memory cards Periodicity analysis Motifs and biological sequence analysis Approximate and consecutive motifs Similarity-based analysis Mining data streams Ordered, time-varying, potentially infinite, data streams 28

Data Mining Functions: (7) Structure andNetwork AnalysisGraph mining Finding frequent subgraphs (e.g., chemical compounds), trees (XML),substructures (web fragments) Information network analysis Social networks: actors (objects, nodes) and relationships (edges) e.g., author networks in CS, terrorist networks Multiple heterogeneous networks A person could be multiple information networks: friends, family, classmates, Links carry a lot of semantic information: Link mining Web mining Web is a big information network: from PageRank to Google Analysis of Web information networks Web community discovery, opinion mining, usage mining, 29

Evaluation of Knowledge One can mine tremendous amount of “patterns” Some may fit only certain dimension space (time, location, ) Some may not be representative, may be transient, 30Are all mined knowledge interesting?Evaluation of mined knowledge directly mine only interesting knowledge? Descriptive vs. predictive Coverage Typicality vs. novelty Accuracy Timeliness

Chapter 1. Introduction31 Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What 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 Mining A Brief History of Data Mining and Data Mining Society Summary

Data Mining: Confluence of Multiple atternRecognitionStatisticsData nceComputing

Why Confluence of Multiple Disciplines?Tremendous amount of data Algorithms must be scalable to handle big data High-dimensionality of data Micro-array may have tens of thousands of dimensions High complexity of data Data streams and sensor data Time-series data, temporal data, sequence data Structure data, graphs, social and information networks Spatial, spatiotemporal, multimedia, text and Web data Software programs, scientific simulations New and sophisticated applications 33

Chapter 1. Introduction34 Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What 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 Mining A Brief History of Data Mining and Data Mining Society Summary

Applications of Data Mining Web page analysis: classification, clustering, ranking Collaborative analysis & recommender systems Basket data analysis to targeted marketing Biological and medical data analysis Data mining and software engineering Data mining and text analysis Data mining and social and information network analysis Built-in (invisible data mining) functions in Google, MS, Yahoo!, Linked, Facebook, Major dedicated data mining systems/tools 35SAS, MS SQL-Server Analysis Manager, Oracle Data Mining Tools)

Chapter 1. Introduction36 Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What 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 Mining A Brief History of Data Mining and Data Mining Society Summary

Major Issues in Data Mining (1) Mining various and new kinds of knowledge Mining knowledge in multi-dimensional space Data mining: An interdisciplinary effort Boosting the power of discovery in a networked environment Handling noise, uncertainty, and incompleteness of data Pattern evaluation and pattern- or constraint-guided mining 37Mining MethodologyUser Interaction Interactive mining Incorporation of background knowledge Presentation and visualization of data mining results

Major Issues in Data Mining (2) Efficiency and scalability of data mining algorithms Parallel, distributed, stream, and incremental mining methods Diversity of data types Handling complex types of data Mining dynamic, networked, and global data repositories 38Efficiency and ScalabilityData mining and society Social impacts of data mining Privacy-preserving data mining Invisible data mining

Chapter 1. Introduction39 Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What 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 Mining A Brief History of Data Mining and Data Mining Society Summary

A Brief History of Data Mining Society1989 IJCAI Workshop on Knowledge Discovery in Databases Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991) 1991-1994 Workshops on Knowledge Discovery in Databases Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. PiatetskyShapiro, P. Smyth, and R. Uthurusamy, 1996) 1995-1998 International Conferences on Knowledge Discovery in Databases andData Mining (KDD’95-98) Journal of Data Mining and Knowledge Discovery (1997) ACM SIGKDD conferences since 1998 and SIGKDD Explorations More conferences on data mining PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001),WSDM (2008), etc. ACM Transactions on KDD (2007) 40

Conferences and Journals on Data MiningKDD Conferences ACM SIGKDD Int. Conf. on KnowledgeDiscovery in Databases and DataMining (KDD) SIAM Data Mining Conf. (SDM) (IEEE) Int. Conf. on Data Mining (ICDM) European Conf. on Machine Learningand Principles and practices ofKnowledge Discovery and Data Mining(ECML-PKDD) Pacific-Asia Conf. on KnowledgeDiscovery and Data Mining (PAKDD) Int. Conf. on Web Search and DataMining (WSDM) 41 Other related conferences DB conferences: ACM SIGMOD,VLDB, ICDE, EDBT, ICDT, Web and IR conferences: WWW,SIGIR, WSDM ML conferences: ICML, NIPS PR conferences: CVPR,Journals Data Mining and KnowledgeDiscovery (DAMI or DMKD) IEEE Trans. On Knowledge and DataEng. (TKDE) KDD Explorations ACM Trans. on KDD

Where to Find References? DBLP, CiteSeer, Google 42 Data mining and KDD (SIGKDD)Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc.Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDDDatabase systems (SIGMOD)Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAAJournals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc.AI & Machine LearningConferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc.Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems, IEEE-PAMI, etc.Web and IRConferences: SIGIR, WWW, CIKM, etc.Journals: WWW: Internet and Web Information Systems,StatisticsConferences: Joint Stat. Meeting, etc.Journals: Annals of statistics, etc.VisualizationConference proceedings: CHI, ACM-SIGGraph, etc.Journals: IEEE Trans. visualization and computer graphics, etc.

Chapter 1. Introduction43 Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What 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 Mining A Brief History of Data Mining and Data Mining Society Summary

Summary44 Data mining: Discovering interesting patterns and knowledge from massive amountof data A natural evolution of science and information technology, in great demand, withwide applications A KDD process includes data cleaning, data integration, data selection,transformation, data mining, pattern evaluation, and knowledge presentation Mining can be performed in a variety of data Data mining functionalities: characterization, discrimination, association,classification, clustering, trend and outlier analysis, etc. Data mining technologies and applications Major issues in data mining

Recommended Reference Books 45Charu C. Aggarwal, Data Mining: The Textbook, Springer, 2015E. Alpaydin. Introduction to Machine Learning, 2nd ed., MIT Press, 2011R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and KnowledgeDiscovery, Morgan Kaufmann, 2001J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. Morgan Kaufmann, 3rd ed. ,2011T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference,and Prediction, 2nd ed., Springer, 2009T. M. Mitchell, Machine Learning, McGraw Hill, 1997P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005 (2nd ed. 2016)I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with JavaImplementations, Morgan Kaufmann, 2nd ed. 2005Mohammed J. Zaki and Wagner Meira Jr., Data Mining and Analysis: Fundamental Concepts andAlgorithms 2014

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Aug 28, 2017 · Data Mining vs. Data Exploration Which view do you prefer? KDD vs. ML/Stat. vs. Business Intelligence Depending on the data, applications, and your focus Data Mining vs. Data Exploration Business intelligence view Warehouse, data cube, reporting but not much mining Business object