Data Mining: Concepts and Techniques

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Data Mining: oncepts and Techniques — Slides for Textbook — — Chapter 1 — ©Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab School of Computing Science Simon Fraser University, Canada http://www.cs.sfu.ca December 24, 2024 Peete see nett 

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Acknowledgements = This work on this set of slides started with my (Han’s) tutorial for UCLA Extension course in February 1998 Dr. Hongjun Lu from Hong Kong Univ. of Science and Technology taught jointly with me a Data Mining Summer Course in Shanghai, China in July 1998. He has contributed many excellent slides to it Some graduate students have contributed many new slides in the following years. Notable contributors include Eugene Belchev, Jian Pei, and Osmar R. Zaiane (now teaching in Univ. of Alberta). December 24, 2024 Peete see nett 

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CMPT-459-00.3 Course Schedule ع يي ‎eats ace‏ رت ا ا ‎eur ee eerie ia Ree nage‏ 0 ‎eee ence eee Ae ea)‏ 00 ۱ الاك عع ‎ee Oe a ee oe ara‏ ع ۰ ‎u‏ ‎Peon e se tM eee eT‏ ‎Ree es eR‏ اي ‎ee Mec: ee‏ 0 ‎eeeg‏ ل ا ‎ee Sere Sen Reco‏ 0 ييف رت ۱ ۱ ۱ عذال #2 6ارونيعممون! 13 ۳۹ ل ل ل 0 ‎aa‏ ا 00 ‎ess‏ ۱ ۷24 ل 02 3, WS: December 24, 2024 Peete see nett 

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Where to Find the Set of 1 Slides? = Tutorial sections (MS PowerPoint files): = http://www.cs.sfu.ca/~han/dmbook = Other conference presentation slides (.ppt): "= http://db.cs.sfu.ca/ or http://www.cs.sfu.ca/~han = Research papers, DBMiner system, and other related information: = http://db.cs.sfu.ca/ or http://www.cs.sfu.ca/~han December 24, 2024 Peete see nett 

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Chapter 1. Introduction د = Motivation: 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 December 24, 2024 Peete see nett 

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Motivation: “Necessity is the Mother of Invention” = Data explosion problem = Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories = We are drowning in data, but starving for knowledge! = Solution: Data warehousing and data mining * Data warehousing and on-line analytical processing = Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases December 24, 2024 Peete see nett 

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Evolution of Database Technology (See Fig. 1.1) = 1960s: " Data collection, database creation, IMS and network DBMS = 1970s: = Relational data model, relational DBMS implementation = 1980s: = RDBMS, advanced data models (extended-relational, OO, deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc.) = 1990s—2000s: = Data mining and data warehousing, multimedia databases, and Web databases December 24, 2024 Peete see nett 

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What Is Data Mining? = Data mining (knowledge discovery in databases): = Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases » ‏رورت رو را ریات عبالأهممعغام‎ = Data mining: a misnomer? = Knowledge discovery(mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. = What is not data mining? = (Deductive) query processing. = Expert systems or small ML/statistical programs December 24, 2024 Peete see nett 8 

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Why Data Mining? — Potential Applications = Database analysis and decision support 1۱ Rene naa = target marketing, customer relation management, market basket analysis, cross selling, market segmentation ۱ 8 = Forecasting, customer retention, improved underwriting, quality control, competitive analysis 0 ce eRe etait Rue eye aia = Other Applications * Text mining (news group, email, documents) and Web analysis. Saale uae sae nt) December 24, 2024 Peete see nett 

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Market Analysis and Management (1) 57أكلااة36 106 5عع]ناه5 03638 عط مخ عمعطلالا " = Credit card transactions, loyalty cards, discount coupons, customer complaint 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 = Conversion of single to a joint bank account: marriage, etc. = Cross-market analysis = Associations/co-relations between product sales = Prediction based on the association information December 24, 2024 Peete see nett 10 

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11 Market Analysis and Management (2) = Customer profiling = data mining can tell you what types of customers buy what products (clustering or classification) = Identifying customer requirements = identifying the best products for different customers = use prediction to find what factors will attract new customers ‏ات ریات ات مرها‎ = various multidimensional summary reports = statistical summary information (data central tendency and ‏تیان‎ December 24, 2024 Peete see nett 

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Corporate Analysis and Risk Management = Finance planning and asset evaluation = cash flow analysis and prediction = contingent claim analysis to evaluate 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 a highly competitive market December 24, 2024 Peete see nett 12 

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Fraud Detection and Management (1) pplications = widely used in health care, retail, credit card services, telecommunications (phone card fraud), etc. = Approach = use historical data to build models of fraudulent behavior and use data mining to help identify similar instances = Examples = auto insurance: detect a group of people who stage accidents to collect on insurance = money laundering: detect suspicious money transactions (US Treasury's Financial Crimes Enforcement Network) = medical insurance: detect professional patients and ring of doctors and ring of references December 24, 2024 Peete see nett 13 

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Fraud Detection and Management (2) etecting inappropriate medical treatment قط روت رن یتیگ ریا تا و۱۱ ‎in many cases blanket screening tests were requested‏ ‎(save Australian $1m/yr).‏ = Detecting telephone fraud = Telephone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm. = British Telecom identified discrete groups of callers with frequent intra-group calls, especially mobile phones, and broke a multimillion dollar fraud. ‏الت بن‎ = Analysts estimate that 38% of retail shrink is due to dishonest employees. December 24, 2024 Peete see nett 14 

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Other Applications ports = IBM Advanced Scout analyzed NBA game statistics (shots blocked, assists, and 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 وت روت زو = IBM Surf-Aid applies data mining algorithms to Web access logs for market-related pages to discover customer preference and behavior pages, analyzing effectiveness of Web marketing, improving Web site organization, etc. December 24, 2024 Peete see nett 15 

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Data Mining: A KDD Process نی ۱ 16 PVCu UT sl = Data mining: the core or (۵ 58 iscovery process. Data ‏ين‎ BE ‏نت تا‎ ۳ 22 an ات نا الي ا للا دونو م قل preter ty December 24, 2924 Peete see nett 

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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: (may take 60% of effort!) = Data reduction and transformation: Se Ree tM a sae ‏ی‎ CS ‏لورت‎ = Choosing functions of data mining Se eye ‏ا ا ا‎ elec len = Choosing the mining algorithm(s) = Data mining: search for patterns of interest = Pattern evaluation and knowledge presentation Se EE ester UC le Res Ue ec a = Use of discovered knowledge December 24, 2024 Peete see nett 17 

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Data Mining and Business و :۱ ‎etc‏ ان ‎aking ‎000 ‎Data Presentatio ‏تا‎ ‎72 2 = PUT ] Te AU ‏هد نا ‏عونمم | ‏ماد ‎Bee UME CRED‏ اکتا ‎ ‎ ‎ ‎ ‎w meee Cla‏ ا ‎OLAP, MDA 9‏ ‎Data Sources PAper, Files, Information Providers, Database Systems, OLTP ‎Peete nett ‎December 24, 2024 ‎ ‎ 

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Architecture of a Typical Data Mining System Seen user interface 1 1 ۵ ۲6۲۱ا۵ظ ! + Data mining engine-——— 1 Database or data warehouse Data clea: es ۵ Filteri peters ۱ Knowledge- 0 December 24, 2024 Peete hee tt 19 

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Data Mining: On What Kind of Data? = Relational databases = Data warehouses = Transactional databases = Advanced DB and information repositories = Object-oriented and object-relational databases = Spatial databases = Time-series data and temporal data = Text databases and multimedia databases = Heterogeneous and legacy databases = WWW December 24, 2024 Peete see nett 20 

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Data Mining Functionalities (1) = Concept description: Characterization and discrimination = Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet regions = Association (correlation and causality) = Multi-dimensional vs. single-dimensional association = age(X, “20..29”) * income(X, “20..29K”) > buys(X, “PC”) [support = 2%, confidence = 60%] = contains(T, “computer”) > contains(x, “software”) ]196, 75961 December 24, 2024 Peete see nett 21 

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22 Data Mining Functionalities (2) lassification and Prediction = Finding 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, neural network = Prediction: Predict some unknown or missing numerical Nelle} = Cluster analysis = Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns = Clustering based on the principle: maximizing the intra-class similarity and minimizing the interclass similarity December 24, 2024 Peete see nett 

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Data Mining Functionalities (3) = Outlier analysis = Outlier: a data object that does not comply with the general ‏عط كه ءمأناق عط‎ 33 It can be considered as noise or exception but is quite useful in fraud detection, rare events analysis = Trend and evolution analysis = Trend and deviation: regression analysis = Sequential pattern mining, periodicity analysis = Similarity-based analysis = Other pattern-directed or statistical analyses December 24, 2024 Peete ee ert 23 

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Are All the “Discovered” Patterns Interesting? data mining system/query may generate thousands of patterns, not all of them are interesting. Beech tee aimee cement NAS Ete M ‏ا‎ = 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. subjective interestingness measures: Same Tsetse eC Rett: Ree Coe ese ect aa 010100 ee ee See tetera ee Renee aR Coa teeta 00000 December 24, 2024 Peete see nett 24 

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Can We Find All and Only Interesting Patterns? * Find all the interesting patterns: Completeness = Can a data mining system find all the interesting patterns? = Association vs. classification vs. clustering = Search for only interesting patterns: Optimization = Can a data mining system find only the interesting ۱01۶ ‏فتووت‎ ‎= Approaches * First general all the patterns and then filter out the ۱1 ‏ی رات توت زوا زر‎ = Generate only the interesting patterns—mining query optimization ee ey eles) eee entry 25 

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Data Mining: Confluence of ۳ Multiple Disciplines 9 December 24, 2024 Peete see nett 

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27 Data Mining: Classification ۳ Schemes = General functionality = Descriptive data mining = Predictive data mining = Different views, different classifications = Kinds of databases to be mined = Kinds of knowledge to be discovered = Kinds of techniques utilized = Kinds of applications adapted December 24, 2024 Peete see nett 

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۱۰۹ ۱0۱۱۹۵ ‏و0۱‎ ۱ Data Mining Classification ۲۳۱۰۰۱۰۰۰ ‏ایا‎ ‎= Relational, transactional, object-oriented, object-relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW, etc. = Knowledge to be mined = Characterization, discrimination, association, classification, clustering, trend, deviation and outlier analysis, etc. = Multiple/integrated functions and mining at multiple levels =" Techniques utilized = Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, neural network, etc. = Applications adapted * Retail, telecommunication, banking, fraud analysis, DNA mining, ‏.ععة ,وتنزاهمة وهامع للا ,ومتمتد طعلالا ,كتدزاهم3 غع6/مهمم عاء0غد‎ December 24, 2024 Peete see nett 28 

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OLAP Mining: An Integration of Data 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 knowledge and patterns at different levels of abstraction by drilling/rolling, pivoting, slicing/dicing, etc. = Integration of multiple mining functions = Characterized classification, first clustering and then association December 24, 2024 Peete see nett 29 

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Data Repository, An OLAM Architecture lining qu OLAM Engine Wareho use 

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Major Issues in Data Mining (1) = Mining methodology and user interaction Mining different kinds of knowledge in databases Interactive mining of knowledge at multiple levels of abstraction Incorporation of background knowledge Data mining query languages and ad-hoc data mining Expression and visualization of data mining results Handling noise and incomplete data Pattern evaluation: the interestingness problem = Performance and scalability Efficiency and scalability of data mining algorithms Parallel, distributed and incremental mining methods December 24, 2024 Peete ‏ا‎ 31 

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32 Major Issues in Data Mining (2) = Issues relating to the diversity of data types = Handling relational and complex types of data = Mining information from heterogeneous databases and global information systems (WWW) = Issues related to applications and social impacts = Application of discovered knowledge " Domain-specific data mining tools = Intelligent query answering = Process control and decision making = Integration of the discovered knowledge with existing ۱ ‏وتات‎ ‎= Protection of data security, integrity, and privacy December 24, 2024 Peete see nett 

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Summary = Data mining: discovering interesting patterns from large amounts 06 = Anatural evolution of database technology, in great demand, with wide applications » AKDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation = Mining can be performed in a variety of information repositories = Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc. ™ Classification of data mining systems = Major issues in data mining December 24, 2024 Peete see nett 33 

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A Brief History of Data Mining Society = 1989 IJCAI Workshop on Knowledge Discovery in Databases Piatetsky-Shapiro * 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. Piatetsky- Seu meu Miata) = 1995-1998 International Conferences on Knowledge Discovery in Databases and Data Mining (KDD'95-98 ۱ = 1998 ACM SIGKDD, SIGKDD'1999-2001 conferences, and SIGKDD Explorations = More conferences on data mining SOP MU a ‏ل‎ ‎December 24, 2024 Data Mining: Concepts and Techniques 34 

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Where to Find References? = Data mining and KDD (SIGKDD member CDROM): = Conference proceedings: KDD, and others, such as PKDD, PAKDD, etc. Sas Pe MarR R title apere niin = Database field (SIGMOD member CD ROM): = Conference proceedings: ACM-SIGMOD, ACM-PODS, VLDB, ICDE, EDBT, DASFAA = Journals: ACM-TODS, J. ACM, IEEE-TKDE, JIS, etc. = Aland Machine Learning: = Conference proceedings: Machine learning, AAAI, IJCAI, etc. ۱ ۰۱۱ = Conference proceedings: Joint Stat. Meeting, etc. Se Or eee Gut ‏ا‎ ‎= Visualization: * Conference proceedings: CHI, etc. ۱ ‏یر‎ Rectal ieciae cc] al ecmecic December 24, 2024 Peete see nett 35 

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References SOM ‏ا‎ Cee FONE ee GC ea RR CCU er eC See CCU eC ec CR eee Se Ra ome ۱ Se CeCe eee cat Rn tele ۱ EL Tne SECM Ac Sec RNC RU en RC RR Pere ee eae cn URN ROR NC cree ree ee ‏تا‎ SRM AEC eo RNs a ‏ا‎ Soe EN ago December 24, 2024 Peete see nett 36 

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http://www.cs.sfu.ca/ 93 ~han 

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CMPT-843 Course Arrangement Para ee Be ercetece Catia SP BCR ‏ا‎ ‎۱۳۳ ear ets alee cee ce Cem og Ue Tela ا 0 00 runt Smee cece Re ee cc Ya See ene ELLs Seno gee et eee ee ES 00 Hetil Deadline for the selection of your work in the semester: ۱ ect ea Ree aes Sees ‏ا‎ est aca ce Reco Sees et eee oe Rema gto 0 0 ‏ل‎ ‎۱ ‎0 Rem aaah December 24, 2024 Peete see nett 38 

Data Mining: Concepts and Techniques — Slides for Textbook — — Chapter 1 — ©Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab School of Computing Science Simon Fraser University, Canada http://www.cs.sfu.ca March 16, 2025 Data Mining: Concepts and Techniques 1 Acknowledgements  This work on this set of slides started with my (Han’s) tutorial for UCLA Extension course in February 1998  Dr. Hongjun Lu from Hong Kong Univ. of Science and Technology taught jointly with me a Data Mining Summer Course in Shanghai, China in July 1998. He has contributed many excellent slides to it  Some graduate students have contributed many new slides in the following years. Notable contributors include Eugene Belchev, Jian Pei, and Osmar R. Zaiane (now teaching in Univ. of Alberta). March 16, 2025 Data Mining: Concepts and Techniques 2 CMPT-459-00.3 Course Schedule            Chapter 1. Introduction {W1:L2, L3} Chapter 2. Data warehousing and OLAP technology for data mining {W2:L1-3, W3:L1-2}  Homework # 1 distribution (SQLServer7.0+ DBMiner2.0) Chapter 3. Data preprocessing {W3:L3, W4: L1-L2} Chapter 4. Data mining primitives, languages and system architectures {W4: L3, W5: L1}  Homework #1 due, homework #2 distribution Chapter 5. Concept description: Characterization and comparison {W5: L2, L3, W6: L2}  W6:L1 Thanksgiving Day Chapter 6. Mining association rules in large databases {W6: L3, W7: L1-3, W8: L2}  Midterm {W8: L2} Chapter 7. Classification and prediction {W8:L3, W9: L1-L3} Chapter 8. Clustering analysis {W10: L1-L3}  W10: L3 Homework #2 due Chapter 9. Mining complex types of data {W11: L2-L3, W12:L1-L3}  W11:L1 Remembrance Day, W12:L3 Course project due Chapter 10. Data mining applications and trends in data mining {W13: L1-L3} Final Exam (W14) March 16, 2025 Data Mining: Concepts and Techniques 3 Where to Find the Set of Slides?  Tutorial sections (MS PowerPoint files):   Other conference presentation slides (.ppt):   http://www.cs.sfu.ca/~han/dmbook http://db.cs.sfu.ca/ or http://www.cs.sfu.ca/~han Research papers, DBMiner system, and other related information:  March 16, 2025 http://db.cs.sfu.ca/ or http://www.cs.sfu.ca/~han Data Mining: Concepts and Techniques 4 Chapter 1. Introduction  Motivation: 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 March 16, 2025 Data Mining: Concepts and Techniques 5 Motivation: “Necessity is the Mother of Invention”  Data explosion problem  Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories  We are drowning in data, but starving for knowledge!  Solution: Data warehousing and data mining  Data warehousing and on-line analytical processing  Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases March 16, 2025 Data Mining: Concepts and Techniques 6 Evolution of Database Technology (See Fig. 1.1)  1960s:   1970s:   Relational data model, relational DBMS implementation 1980s:   Data collection, database creation, IMS and network DBMS RDBMS, advanced data models (extended-relational, OO, deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc.) 1990s—2000s:  Data mining and data warehousing, multimedia databases, and Web databases March 16, 2025 Data Mining: Concepts and Techniques 7 What Is Data Mining?  Data mining (knowledge discovery in databases):   Alternative names and their “inside stories”:    Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases Data mining: a misnomer? Knowledge discovery(mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. What is not data mining?   March 16, 2025 (Deductive) query processing. Expert systems or small ML/statistical programs Data Mining: Concepts and Techniques 8 Why Data Mining? — Potential Applications  Database analysis and decision support  Market analysis and management   Risk analysis and management    target marketing, customer relation management, market basket analysis, cross selling, market segmentation Forecasting, customer retention, improved underwriting, quality control, competitive analysis Fraud detection and management Other Applications  Text mining (news group, email, documents) and Web analysis.  Intelligent query answering March 16, 2025 Data Mining: Concepts and Techniques 9 Market Analysis and Management (1)  Where are the data sources for analysis?   Target marketing   Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc. Determine customer purchasing patterns over time   Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies Conversion of single to a joint bank account: marriage, etc. Cross-market analysis  Associations/co-relations between product sales  Prediction based on the association information March 16, 2025 Data Mining: Concepts and Techniques 10 Market Analysis and Management (2)  Customer profiling  data mining can tell you what types of customers buy what products (clustering or classification)   Identifying customer requirements  identifying the best products for different customers  use prediction to find what factors will attract new customers Provides summary information  various multidimensional summary reports  statistical summary information (data central tendency and variation) March 16, 2025 Data Mining: Concepts and Techniques 11 Corporate Analysis and Risk Management  Finance planning and asset evaluation     Resource planning:   cash flow analysis and prediction contingent claim analysis to evaluate assets cross-sectional and time series analysis (financial-ratio, trend analysis, etc.) 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 a highly competitive market March 16, 2025 Data Mining: Concepts and Techniques 12 Fraud Detection and Management (1)  Applications   Approach   widely used in health care, retail, credit card services, telecommunications (phone card fraud), etc. use historical data to build models of fraudulent behavior and use data mining to help identify similar instances Examples    auto insurance: detect a group of people who stage accidents to collect on insurance money laundering: detect suspicious money transactions (US Treasury's Financial Crimes Enforcement Network) medical insurance: detect professional patients and ring of doctors and ring of references March 16, 2025 Data Mining: Concepts and Techniques 13 Fraud Detection and Management (2)  Detecting inappropriate medical treatment   Detecting telephone fraud    Australian Health Insurance Commission identifies that in many cases blanket screening tests were requested (save Australian $1m/yr). Telephone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm. British Telecom identified discrete groups of callers with frequent intra-group calls, especially mobile phones, and broke a multimillion dollar fraud. Retail  Analysts estimate that 38% of retail shrink is due to dishonest employees. March 16, 2025 Data Mining: Concepts and Techniques 14 Other Applications  Sports   Astronomy   IBM Advanced Scout analyzed NBA game statistics (shots blocked, assists, and fouls) to gain competitive advantage for New York Knicks and Miami Heat 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 preference and behavior pages, analyzing effectiveness of Web marketing, improving Web site organization, etc. March 16, 2025 Data Mining: Concepts and Techniques 15 Data Mining: A KDD Process Pattern Evaluation  Data mining: the core of knowledge discovery process. Data Mining Task-relevant Data Data Warehouse Selection Data Cleaning Data Integration Databas March 16, 2025es Data Mining: Concepts and Techniques 16 Steps of a KDD Process  Learning the application domain:     Creating a target data set: data selection Data cleaning and preprocessing: (may take 60% of effort!) Data reduction and transformation:     summarization, classification, regression, association, clustering. Choosing the mining algorithm(s) Data mining: search for patterns of interest Pattern evaluation and knowledge presentation   Find useful features, dimensionality/variable reduction, invariant representation. Choosing functions of data mining   relevant prior knowledge and goals of application visualization, transformation, removing redundant patterns, etc. Use of discovered knowledge March 16, 2025 Data Mining: Concepts and Techniques 17 Data Mining and Business Intelligence Increasing potential to support business decisions Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery End User Business Analyst Data Analyst Data Statistical Analysis, Querying and Reporting Exploration Data Warehouses / Data Marts OLAP, MDA DBA Data Sources Paper, Files, Information Providers, Database Systems, OLTP March 16, 2025 Data Mining: Concepts and Techniques 18 Architecture of a Typical Data Mining System Graphical user interface Pattern evaluation Data mining engine Database or data warehouse Filteri Data cleaningserver & data ng integration Databas es March 16, 2025 Knowledgebase Data Wareho use Data Mining: Concepts and Techniques 19 Data Mining: On What Kind of Data?     Relational databases Data warehouses Transactional databases Advanced DB and information repositories       March 16, 2025 Object-oriented and object-relational databases Spatial databases Time-series data and temporal data Text databases and multimedia databases Heterogeneous and legacy databases WWW Data Mining: Concepts and Techniques 20 Data Mining Functionalities (1)  Concept description: Characterization and discrimination   Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet regions Association (correlation and causality)    Multi-dimensional vs. single-dimensional association age(X, “20..29”) ^ income(X, “20..29K”)  buys(X, “PC”) [support = 2%, confidence = 60%] contains(T, “computer”)  contains(x, “software”) [1%, 75%] March 16, 2025 Data Mining: Concepts and Techniques 21 Data Mining Functionalities (2)  Classification and Prediction  Finding 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, neural network Prediction: Predict some unknown or missing numerical values Cluster analysis  Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns  Clustering based on the principle: maximizing the intra-class similarity and minimizing the interclass similarity March 16, 2025 Data Mining: Concepts and Techniques 22 Data Mining Functionalities (3)  Outlier analysis  Outlier: a data object that does not comply with the general behavior of the data  It can be considered as noise or exception but is quite useful in fraud detection, rare events analysis   Trend and evolution analysis  Trend and deviation: regression analysis  Sequential pattern mining, periodicity analysis  Similarity-based analysis Other pattern-directed or statistical analyses March 16, 2025 Data Mining: Concepts and Techniques 23 Are All the “Discovered” Patterns Interesting?  A data mining system/query may generate thousands of patterns, not all of them are interesting.   Suggested approach: 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. subjective interestingness measures:  Objective: based on statistics and structures of patterns, e.g., support, confidence, etc.  Subjective: based on user’s belief in the data, e.g., unexpectedness, novelty, actionability, etc. March 16, 2025 Data Mining: Concepts and Techniques 24 Can We Find All and Only Interesting Patterns?   Find all the interesting patterns: Completeness  Can a data mining system find all the interesting patterns?  Association vs. classification vs. clustering Search for only interesting patterns: Optimization   Can a data mining system find only the interesting patterns? Approaches   March 16, 2025 First general all the patterns and then filter out the uninteresting ones. Generate only the interesting patterns—mining query optimization Data Mining: Concepts and Techniques 25 Data Mining: Confluence of Multiple Disciplines Database Technology Machine Learning Information Science March 16, 2025 Statistics Data Mining Visualization Other Disciplines Data Mining: Concepts and Techniques 26 Data Mining: Classification Schemes   General functionality  Descriptive data mining  Predictive data mining Different views, different classifications March 16, 2025  Kinds of databases to be mined  Kinds of knowledge to be discovered  Kinds of techniques utilized  Kinds of applications adapted Data Mining: Concepts and Techniques 27 A Multi-Dimensional View of Data Mining Classification     Databases to be mined  Relational, transactional, object-oriented, object-relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW, etc. Knowledge to be mined  Characterization, discrimination, association, classification, clustering, trend, deviation and outlier analysis, etc.  Multiple/integrated functions and mining at multiple levels Techniques utilized  Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, neural network, etc. Applications adapted  Retail, telecommunication, banking, fraud analysis, DNA mining, stock market analysis, Web mining, Weblog analysis, etc. March 16, 2025 Data Mining: Concepts and Techniques 28 OLAP Mining: An Integration of Data Mining and Data Warehousing  Data mining systems, DBMS, Data warehouse systems coupling   On-line analytical mining data   integration of mining and OLAP technologies Interactive mining multi-level knowledge   No coupling, loose-coupling, semi-tight-coupling, tight-coupling Necessity of mining knowledge and patterns at different levels of abstraction by drilling/rolling, pivoting, slicing/dicing, etc. Integration of multiple mining functions  Characterized classification, first clustering and then association March 16, 2025 Data Mining: Concepts and Techniques 29 An OLAM Architecture Mining query OLAM Engine Mining result User GUI API OLAP Engine Layer4 User Interface Layer3 OLAP/ OLAM Data Cube API MDD B Filtering&Integra Database API tion Data cleaning Databas es March 16, 2025 Layer2 MDDB Meta Data Filteri ng Data Wareho Data Data Mining: Concepts anduse Techniques integration Layer1 Data Repository 30 Major Issues in Data Mining (1)  Mining methodology and user interaction    Mining different kinds of knowledge in databases Interactive mining of knowledge at multiple levels of abstraction  Incorporation of background knowledge  Data mining query languages and ad-hoc data mining  Expression and visualization of data mining results  Handling noise and incomplete data  Pattern evaluation: the interestingness problem Performance and scalability  Efficiency and scalability of data mining algorithms  Parallel, distributed and incremental mining methods March 16, 2025 Data Mining: Concepts and Techniques 31 Major Issues in Data Mining (2)  Issues relating to the diversity of data types    Handling relational and complex types of data Mining information from heterogeneous databases and global information systems (WWW) Issues related to applications and social impacts    Application of discovered knowledge  Domain-specific data mining tools  Intelligent query answering  Process control and decision making Integration of the discovered knowledge with existing knowledge: A knowledge fusion problem Protection of data security, integrity, and privacy March 16, 2025 Data Mining: Concepts and Techniques 32 Summary  Data mining: discovering interesting patterns from large amounts of data  A natural evolution of database technology, in great demand, with wide 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 information repositories  Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc.  Classification of data mining systems  Major issues in data mining March 16, 2025 Data Mining: Concepts and Techniques 33 A Brief History of Data Mining Society  1989 IJCAI Workshop on Knowledge Discovery in Databases (Piatetsky-Shapiro)   1991-1994 Workshops on Knowledge Discovery in Databases   Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991) 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 and Data Mining (KDD’95-98)  Journal of Data Mining and Knowledge Discovery (1997)  1998 ACM SIGKDD, SIGKDD’1999-2001 conferences, and SIGKDD Explorations  More conferences on data mining  PAKDD, PKDD, SIAM-Data Mining, (IEEE) ICDM, etc. March 16, 2025 Data Mining: Concepts and Techniques 34 Where to Find References?  Data mining and KDD (SIGKDD member CDROM):    Database field (SIGMOD member CD ROM):     Conference proceedings: Machine learning, AAAI, IJCAI, etc. Journals: Machine Learning, Artificial Intelligence, etc. Statistics:    Conference proceedings: ACM-SIGMOD, ACM-PODS, VLDB, ICDE, EDBT, DASFAA Journals: ACM-TODS, J. ACM, IEEE-TKDE, JIIS, etc. AI and Machine Learning:   Conference proceedings: KDD, and others, such as PKDD, PAKDD, etc. Journal: Data Mining and Knowledge Discovery Conference proceedings: Joint Stat. Meeting, etc. Journals: Annals of statistics, etc. Visualization:   Conference proceedings: CHI, etc. Journals: IEEE Trans. visualization and computer graphics, etc. March 16, 2025 Data Mining: Concepts and Techniques 35 References  U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996.  J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2000.  T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Communications of ACM, 39:58-64, 1996.  G. Piatetsky-Shapiro, U. Fayyad, and P. Smith. From data mining to knowledge discovery: An overview. In U.M. Fayyad, et al. (eds.), Advances in Knowledge Discovery and Data Mining, 1-35. AAAI/MIT Press, 1996.  G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991. March 16, 2025 Data Mining: Concepts and Techniques 36 http://www.cs.sfu.ca/ ~han Thank you !!! March 16, 2025 Data Mining: Concepts and Techniques 37 CMPT-843 Course Arrangement     1st week: full instructor teaching 2nd to 11th week: 1/2 graduate student + 1/2 instructor teaching 12-13th week: full student graduate project presentation Course evaluation:      presentation (quality of presentation slides 7% + presentation 8%) 15% midterm exam 35% project (presentation 5% + report 25%) total 30% homework (2): 20% Deadline for the selection of your work in the semester:        selection of course presentation: at the end of the 1st week selection of the course project: at the end of the 3rd week project proposal due date: at the end of the 4th week homework due dates: project due date: end of the semester Your presentation slides due date: one day before the presentation midterm date: end of the 8th week March 16, 2025 Data Mining: Concepts and Techniques 38