1、October 4, 2018,Data Mining: Concepts and Techniques,1,Data Mining: Concepts and Techniques Slides for Textbook Chapter 1 ,Jiawei Han and Micheline Kamber Department of Computer Science University of Illinois at Urbana-Champaign www.cs.uiuc.edu/hanj,October 4, 2018,Data Mining: Concepts and Techniqu
2、es,2,Data Mining: Concepts and Techniques,October 4, 2018,Data Mining: Concepts and Techniques,3,Acknowledgements,This set of slides started with Hans tutorial for UCLA Extension course in February 1998 Other subsequent contributors: Dr. Hongjun Lu (Hong Kong Univ. of Science and Technology) Graduat
3、e students from Simon Fraser Univ., Canada, notably Eugene Belchev, Jian Pei, and Osmar R. Zaiane Graduate students from Univ. of Illinois at Urbana-Champaign,October 4, 2018,Data Mining: Concepts and Techniques,4,CS497JH Schedule (Fall 2002),Chapter 1. Introduction W1:L1 Chapter 2. Data pre-process
4、ing W4: L1-2 Homework # 1 distribution (SQLServer2000) Chapter 3. Data warehousing and OLAP technology for data mining W2:L1-2, W3:L1-2 Homework # 2 distribution Chapter 4. Data mining primitives, languages, and system architectures W5: L1 Chapter 5. Concept description: Characterization and compari
5、son W5: L2, W6: L1 Chapter 6. Mining association rules in large databases W6:L2, W7:L1-L21, W8: L1 Homework #3 distribution Chapter 7. Classification and prediction W8:L2, W9: L2, W10:L1 Midterm W9: L1 Chapter 8. Clustering analysis W10:L2, W11: L1-2 Homework #4 distribution Chapter 9. Mining comple
6、x types of data W12: L1-2, W13:L1-2 Chapter 10. Data mining applications and trends in data mining W14: L1 Research/Development project presentation (W14-W15 + final exam period) Final Project Due,October 4, 2018,Data Mining: Concepts and Techniques,5,Where to Find the Set of Slides?,Book page: (MS
7、PowerPoint files): www.cs.uiuc.edu/hanj/dmbook Updated course presentation slides (.ppt): www-courses.cs.uiuc.edu/cs497jh/ Research papers, DBMiner system, and other related information: www.cs.uiuc.edu/hanj or ,October 4, 2018,Data Mining: Concepts and Techniques,6,Chapter 1. Introduction,Motivatio
8、n: Why data mining? What is data mining? Data Mining: On what kind of data? Data mining functionality Are all the patterns interesting? Classification of data mining systems Major issues in data mining,October 4, 2018,Data Mining: Concepts and Techniques,7,Necessity Is the Mother of Invention,Data e
9、xplosion problem Automated data collection tools and mature database technology lead to tremendous amounts of data accumulated and/or to be analyzed in databases, data warehouses, and other information repositories We are drowning in data, but starving for knowledge! Solution: Data warehousing and d
10、ata mining Data warehousing and on-line analytical processing Miing interesting knowledge (rules, regularities, patterns, constraints) from data in large databases,October 4, 2018,Data Mining: Concepts and Techniques,8,Evolution of Database Technology,1960s: Data collection, database creation, IMS a
11、nd network DBMS 1970s: Relational data model, relational DBMS implementation 1980s: RDBMS, advanced data models (extended-relational, OO, deductive, etc.) Application-oriented DBMS (spatial, scientific, engineering, etc.) 1990s: Data mining, data warehousing, multimedia databases, and Web databases
12、2000s Stream data management and mining Data mining with a variety of applications Web technology and global information systems,October 4, 2018,Data Mining: Concepts and Techniques,9,What Is Data Mining?,Data mining (knowledge discovery from data) Extraction of interesting (non-trivial, implicit, p
13、reviously unknown and potentially useful) patterns or knowledge from huge amount of data Data mining: a misnomer? Alternative names Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intellige
14、nce, etc. Watch out: Is everything “data mining”? (Deductive) query processing. Expert systems or small ML/statistical programs,October 4, 2018,Data Mining: Concepts and Techniques,10,Why Data Mining?Potential Applications,Data analysis and decision support Market analysis and management Target mark
15、eting, customer relationship management (CRM), market basket analysis, cross selling, market segmentation Risk analysis and management Forecasting, customer retention, improved underwriting, quality control, competitive analysis Fraud detection and detection of unusual patterns (outliers) Other Appl
16、ications Text mining (news group, email, documents) and Web mining Stream data mining DNA and bio-data analysis,October 4, 2018,Data Mining: Concepts and Techniques,11,Market Analysis and Management,Where does the data come from? Credit card transactions, loyalty cards, discount coupons, customer co
17、mplaint calls, plus (public) lifestyle studies Target marketing Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc. Determine customer purchasing patterns over time Cross-market analysis Associations/co-relations between product sales,
18、 & prediction based on such association Customer profiling What types of customers buy what products (clustering or classification) Customer requirement analysis identifying the best products for different customers predict what factors will attract new customers Provision of summary information mul
19、tidimensional summary reports statistical summary information (data central tendency and variation),October 4, 2018,Data Mining: Concepts and Techniques,12,Corporate Analysis & Risk Management,Finance planning and asset evaluation cash flow analysis and prediction contingent claim analysis to evalua
20、te assets cross-sectional and time series analysis (financial-ratio, trend analysis, etc.) Resource planning summarize and compare the resources and spending Competition monitor competitors and market directions group customers into classes and a class-based pricing procedure set pricing strategy in
21、 a highly competitive market,October 4, 2018,Data Mining: Concepts and Techniques,13,Fraud Detection & Mining Unusual Patterns,Approaches: Clustering & model construction for frauds, outlier analysis Applications: Health care, retail, credit card service, telecomm. Auto insurance: ring of collisions
22、 Money laundering: suspicious monetary transactions Medical insurance Professional patients, ring of doctors, and ring of references Unnecessary or correlated screening tests Telecommunications: phone-call fraud Phone call model: destination of the call, duration, time of day or week. Analyze patter
23、ns that deviate from an expected norm Retail industry Analysts estimate that 38% of retail shrink is due to dishonest employees Anti-terrorism,October 4, 2018,Data Mining: Concepts and Techniques,14,Other Applications,Sports IBM Advanced Scout analyzed NBA game statistics (shots blocked, assists, an
24、d fouls) to gain competitive advantage for New York Knicks and Miami Heat Astronomy JPL and the Palomar Observatory discovered 22 quasars with the help of data mining Internet Web Surf-Aid IBM Surf-Aid applies data mining algorithms to Web access logs for market-related pages to discover customer pr
25、eference and behavior pages, analyzing effectiveness of Web marketing, improving Web site organization, etc.,October 4, 2018,Data Mining: Concepts and Techniques,15,Data Mining: A KDD Process,Data miningcore of knowledge discovery process,Data Cleaning,Data Integration,Databases,Data Warehouse,Knowl
26、edge,Task-relevant Data,Selection,Data Mining,Pattern Evaluation,October 4, 2018,Data Mining: Concepts and Techniques,16,Steps of a KDD Process,Learning the application domain relevant prior knowledge and goals of application Creating a target data set: data selection Data cleaning and preprocessing
27、: (may take 60% of effort!) Data reduction and transformation Find useful features, dimensionality/variable reduction, invariant representation. Choosing functions of data mining summarization, classification, regression, association, clustering. Choosing the mining algorithm(s) Data mining: search
28、for patterns of interest Pattern evaluation and knowledge presentation visualization, transformation, removing redundant patterns, etc. Use of discovered knowledge,October 4, 2018,Data Mining: Concepts and Techniques,17,Data Mining and Business Intelligence,Increasing potential to support business d
29、ecisions,End User,BusinessAnalyst,Data Analyst,DBA,Making Decisions,Data Presentation,Visualization Techniques,Data Mining,Information Discovery,Data Exploration,OLAP, MDA,Statistical Analysis, Querying and Reporting,Data Warehouses / Data Marts,Data Sources,Paper, Files, Information Providers, Data
30、base Systems, OLTP,October 4, 2018,Data Mining: Concepts and Techniques,18,Architecture: Typical Data Mining System,Data Warehouse,Data cleaning & data integration,Filtering,Databases,Database or data warehouse server,Data mining engine,Pattern evaluation,Graphical user interface,Knowledge-base,Octo
31、ber 4, 2018,Data Mining: Concepts and Techniques,19,Data Mining: On What Kinds of Data?,Relational database Data warehouse Transactional database Advanced database and information repository Object-relational database Spatial and temporal data Time-series data Stream data Multimedia database Heterog
32、eneous and legacy database Text databases & WWW,October 4, 2018,Data Mining: Concepts and Techniques,20,Data Mining Functionalities,Concept description: Characterization and discrimination Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet regions Association (correlation an
33、d causality) Diaper Beer 0.5%, 75% Classification and Prediction Construct models (functions) that describe and distinguish classes or concepts for future prediction E.g., classify countries based on climate, or classify cars based on gas mileage Presentation: decision-tree, classification rule, neu
34、ral network Predict some unknown or missing numerical values,October 4, 2018,Data Mining: Concepts and Techniques,21,Data Mining Functionalities (2),Cluster analysis Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns Maximizing intra-class simi
35、larity & minimizing interclass similarity Outlier analysis Outlier: a data object that does not comply with the general behavior of the data Noise or exception? No! useful in fraud detection, rare events analysis Trend and evolution analysis Trend and deviation: regression analysis Sequential patter
36、n mining, periodicity analysis Similarity-based analysis Other pattern-directed or statistical analyses,October 4, 2018,Data Mining: Concepts and Techniques,22,Are All the “Discovered” Patterns Interesting?,Data mining may generate thousands of patterns: Not all of them are interesting Suggested app
37、roach: Human-centered, query-based, focused mining Interestingness measures A pattern is interesting if it is easily understood by humans, valid on new or test data with some degree of certainty, potentially useful, novel, or validates some hypothesis that a user seeks to confirm Objective vs. subje
38、ctive interestingness measures Objective: based on statistics and structures of patterns, e.g., support, confidence, etc. Subjective: based on users belief in the data, e.g., unexpectedness, novelty, actionability, etc.,October 4, 2018,Data Mining: Concepts and Techniques,23,Can We Find All and Only
39、 Interesting Patterns?,Find all the interesting patterns: Completeness Can a data mining system find all the interesting patterns? Heuristic vs. exhaustive search Association vs. classification vs. clustering Search for only interesting patterns: An optimization problem Can a data mining system find
40、 only the interesting patterns? Approaches First general all the patterns and then filter out the uninteresting ones. Generate only the interesting patternsmining query optimization,October 4, 2018,Data Mining: Concepts and Techniques,24,Data Mining: Confluence of Multiple Disciplines,Data Mining,Da
41、tabase Systems,Statistics,Other Disciplines,Algorithm,Machine Learning,Visualization,October 4, 2018,Data Mining: Concepts and Techniques,25,Data Mining: Classification Schemes,General functionality Descriptive data mining Predictive data mining Different views, different classifications Kinds of da
42、ta to be mined Kinds of knowledge to be discovered Kinds of techniques utilized Kinds of applications adapted,October 4, 2018,Data Mining: Concepts and Techniques,26,Multi-Dimensional View of Data Mining,Data to be mined Relational, data warehouse, transactional, stream, object-oriented/relational,
43、active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW Knowledge to be mined Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc. Multiple/integrated functions and mining at multiple levels Techniques utilized Databas
44、e-oriented, data warehouse (OLAP), machine learning, statistics, visualization, etc. Applications adapted Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, Web mining, etc.,October 4, 2018,Data Mining: Concepts and Techniques,27,OLAP Mining: Integration of D
45、ata Mining and Data Warehousing,Data mining systems, DBMS, Data warehouse systems coupling No coupling, loose-coupling, semi-tight-coupling, tight-coupling On-line analytical mining data integration of mining and OLAP technologies Interactive mining multi-level knowledge Necessity of mining knowledg
46、e and patterns at different levels of abstraction by drilling/rolling, pivoting, slicing/dicing, etc. Integration of multiple mining functionsCharacterized classification, first clustering and then association,October 4, 2018,Data Mining: Concepts and Techniques,28,An OLAM Architecture,Data Warehous
47、e,Meta Data,MDDB,OLAM Engine,OLAP Engine,User GUI API,Data Cube API,Database API,Data cleaning,Data integration,Layer3 OLAP/OLAM,Layer2 MDDB,Layer1 Data Repository,Layer4 User Interface,Filtering&Integration,Filtering,Databases,Mining query,Mining result,October 4, 2018,Data Mining: Concepts and Tec
48、hniques,29,Major Issues in Data Mining,Mining methodology Mining different kinds of knowledge from diverse data types, e.g., bio, stream, Web Performance: efficiency, effectiveness, and scalability Pattern evaluation: the interestingness problem Incorporation of background knowledge Handling noise a
49、nd incomplete data Parallel, distributed and incremental mining methods Integration of the discovered knowledge with existing one: knowledge fusion User interaction Data mining query languages and ad-hoc mining Expression and visualization of data mining results Interactive mining of knowledge at multiple levels of abstraction Applications and social impacts Domain-specific data mining & invisible data mining Protection of data security, integrity, and privacy,
