Data mining: concepts and techniques

Introduction What is Data Mining ? Data Mining: Concepts and Techniques — Slides for Course “Data Mining” — — Chapter 1 — Jiawei Han Necessity Is the Mother of Invention  Data explosion problem  Automated data collection tools, widely used database systems, computerized society, and the Internet lead to tremendous amounts of data accumulated and/or to be analyzed in databases, data warehouses, WWW, 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 (OLAP)  Mining interesting knowledge (rules, regularities, patterns, constraints) from data in large databases Evolution of Database Technology  1960s:    Data collection, database creation, IMS and network DBMS 1970s:  Relational data model, relational DBMS implementation  Tedd Codd (1923-2003)  Structured English Query Language (SEQUEL), SQL 1980s:  Advanced data models (extended-relational, OO, deductive, etc.)  Application-oriented DBMS (spatial, scientific, engineering, etc.) Evolution of Database Technology   1990s:  Data mining, data warehousing, multimedia databases  Web databases (..,Amazon) 2000s  Stream data management and mining  Data mining and its applications  Web technology (XML-based DB, data integration) and global information systems 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 data (interesting patterns?)   Alternative names   Data mining: a misnomer? (erro de nome) Knowledge 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”?  (Deductive) query processing.  Expert systems or small Machine Learning/statistical programs Data Mining: Confluence of Multiple Disciplines Database Technology Machine Learning Algorithm Statistics Data Mining Visualization Other Disciplines Why Data Mining? —Potential Applications  Data analysis and decision support 1. Market analysis and management • 2. Risk analysis and management • 3. 1. Target marketing, customer relationship management (CRM), market basket analysis, cross selling, market segmentation Forecasting, customer retention, improved underwriting, quality control, competitive analysis Fraud detection and detection of unusual patterns (outliers) Other Applications  Text mining (news group, email, documents) and Web mining  Medical data mining  Bioinformatics and bio-data analysis Example 1: Market Analysis and Management  Where does the data come from?   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 Market Analysis and Management  Cross-market analysis—Find associations/corelations between product sales, & predict based on such association  Customer profiling—What types of customers buy what products (clustering or classification)  Customer requirement analysis  Identify the best products for different customers  Predict what factors will attract new customers Example 3: Fraud Detection & Mining Unusual Patterns  Approaches:  Unsupervised Learning: Clustering  Supervised Learning: Neuronal Networks  model construction for frauds  outlier analysis Other applications: Health care, retail, credit card service, telecomm.  Auto insurance: ring of collisions  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   Retail industry (venda a retalho)   Phone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm Analysts estimate that 38% of retail shrink is due to dishonest employees Anti-terrorism Data Mining and Knowledge Discovery (KDD) Process Pattern Evaluation  Data mining—core of knowledge discovery process Data Mining Task-relevant Data Data Warehouse Data Cleaning Data Integration Databases Data Selection Mart & Transformation Steps of a KDD Process (1)  Learning the application domain    Data cleaning and preprocessing: (may take 60% of effort!)  To remove noise and inconsistencies  Includes data integration Data selection   relevant prior knowledge and goals of application Where data relevant to the analysis task are retrieved from the database Data transformation  Where data are transformed or consolidated into forms appropriate for mining, by performing summary or aggregation operations, for instance Steps of a KDD Process (2)   Data mining: search for patterns of interest  Choosing functions of data mining: summarization, classification, regression, association, clustering  Choosing the mining algorithm(s) Pattern evaluation   To identify the truly interesting patterns representing knowledge based on interestingness measures Knowledge presentation  Where visualization and knowledge representation techniques are used to present the mined knowledge to the user Architecture: Typical Data Mining System Graphical User Interface Pattern Evaluation Data Mining Engine Database or Data Warehouse Server data cleaning, integration, and selection Database Data World-Wide Other Info Repositories Warehouse Web Know ledge -Base 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 Exploration Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts OLAP, MDA Data Sources Paper, Files, Information Providers, Database Systems, OLTP DBA Data Mining Functionalities (1)  Multidimensional concept description: Characterization and discrimination   Frequent patterns, association, correlation and causality   Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet regions Smoking  Cancer (Correlation or causality?) 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  Predict some unknown or missing numerical values Data Mining Functionalities (2)  Cluster analysis    Outlier analysis    Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns Maximizing intra-class similarity & minimizing interclass similarity Outlier: Data object that does not comply with the general behavior of the data Noise or exception? Trend and evolution analysis    Trend and deviation: e.g., regression analysis Sequential pattern mining, periodicity analysis Similarity-based analysis Are All the “Discovered” Patterns Interesting?  Data mining 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 userʼs belief in the data, e.g., unexpectedness, novelty, actionability, etc. Can We Find All and Only 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 only the interesting patterns?  Approaches • First general all the patterns and then filter out the uninteresting ones. • Generate only the interesting patterns—mining query optimization Data Mining Task   General functionality  Descriptive data mining  Predictive data mining Different views lead to different classifications  Kinds of data to be mined  Kinds of knowledge to be discovered  Kinds of techniques utilized  Kinds of applications adapted Data Mining from different perspectives  Data to be mined    Knowledge to be mined  Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc.  Multiple/integrated functions and mining at multiple levels Techniques utilized   Object-oriented/relational, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW Database-oriented, data warehouse, machine learning, statistics, visualization, etc. Applications adapted  Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc. Primitives that Define a Data Mining Task (1)  Task-relevant data  Database or data warehouse name, Database tables or data warehouse cubes, Condition for data selection, Relevant attributes or dimensions, Data grouping criteria  Type of knowledge to be mined  Characterization (Categories), Discrimination, Association, Classification/prediction, Clustering, Outlier analysis, etc Primitives that Define a Data Mining Task (2)  Background knowledge    Schema hierarchy (taxonomy) • E.g., street < city < province_or_state < country Set-grouping hierarchy • E.g., {20-39} = young, {40-59} = middle_aged Operation-derived hierarchy • email address: hagonzal@cs.uiuc.edu login-name < department < university < country  Rule-based hierarchy • low_profit_margin (X) <= price(X, P1) and cost (X, P2) and (P1 - P2) < $50 Primitives that Define a Data Mining Task (3)  Pattern interestingness measurements  Simplicity, e.g., (association) rule length, (decision) tree size  Certainty, e.g., confidence, classification reliability or accuracy, certainty factor, rule strength, rule quality, discriminating weight, etc.   Utility: potential usefulness, e.g., support (association), noise threshold (description)  Novelty: not previously known, surprising (used to remove redundant rules) Visualization/presentation of discovered patterns  Different backgrounds/usages may require different forms of representation • E.g., rules, tables, crosstabs, pie/bar chart, etc. • Interactive drill up/down, pivoting, slicing and dicing provide different perspectives to data Why Data Mining Query Language?     Automated vs. query-driven?  Finding all the patterns autonomously in a database?— unrealistic because the patterns could be too many but uninteresting Data mining should be an interactive process  User directs what to be mined Users must be provided with a set of primitives to be used to communicate with the data mining system Incorporating these primitives in a data mining query language  More flexible user interaction  Foundation for design of graphical user interface  Standardization of data mining industry and practice DMQL—A Data Mining Query Language  Motivation  A DMQL can provide the ability to support ad-hoc and interactive data mining  By providing a standardized language like SQL • Hope to achieve a similar effect like that SQL has on relational database • Foundation for system development and evolution • Facilitate information exchange, technology transfer, commercialization and wide acceptance  Design  DMQL is designed with the primitives described earlier An Example Query in DMQL Other Data Mining Languages & Standardization Efforts   Association rule language specifications  MSQL (Imielinski & Virmaniʼ99)  MineRule (Meo Psaila and Ceriʼ96)  Query flocks based on Datalog syntax (Tsur et alʼ98) OLEDB for DM (Microsoftʼ2000) and recently DMX (Microsoft SQLServer 2005)   Based on OLE, OLE DB, OLE DB, C#  Integrating DBMS, data warehouse and data mining DMML (Data Mining Mark-up Language) by DMG (www.dmg.org)  Providing a platform and process structure for effective data mining  Emphasizing on deploying data mining technology to solve business problems Coupling Data Mining with DB/DW Systems  No coupling—flat file processing, not recommended  Loose coupling   Semi-tight coupling—enhanced DM performance   Fetching data from DB/DW Provide efficient implement a few data mining primitives in a DB/ DW system, e.g., sorting, indexing, aggregation, histogram analysis, multiway join, precomputation of some stat functions Tight coupling—A uniform information processing environment  DM is smoothly integrated into a DB/DW system, mining query is optimized based on mining query, indexing, etc. Summary  Data mining: discovering interesting patterns from large amounts of data (DB)  A natural evolution of database technology, in great demand, with wide applications  A KDD process includes data cleaning, data integration (Data Warehouse), data selection (Data Mart), 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.   Subjective, requires expert knowledge Data mining systems and architectures A Brief History of Data Mining Society  1989 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 and Data Mining (KDDʼ95-98) • 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), etc.  ACM Transactions on KDD starting in 2007 Conferences and Journals on Data Mining      ACM SIGKDD Int. Conf. on Knowledge Discovery in Databases and Data Mining (KDD) SIAM Data Mining Conf. (SDM) (IEEE) Int. Conf. on Data Mining (ICDM) Conf. on Principles and practices of Knowledge Discovery and Data Mining (PKDD) Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD)  Journals:  Data Mining and Knowledge Discovery (DAMI or DMKD)  IEEE Trans. On Knowledge and Data Eng. (TKDE)  KDD Explorations  ACM Trans. on KDD Where to Find References?—DBLP, CiteSeer, Google  Data mining and KDD (SIGKDD: CDROM)    Database systems (SIGMOD: ACM SIGMOD Anthology—CD ROM)    Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc. Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA Journals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc. AI & Machine Learning   Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc. Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems, IEEE-PAMI, etc. Recommended Reference Books  S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan Kaufmann, 2002  R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000  T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003  U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996  U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2001  J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2nd ed., 2006  D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001  T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2001  T. M. Mitchell, Machine Learning, McGraw Hill, 1997  G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991  P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005  S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998  I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed. 2005 Data Warehousing and OLAP Technology http://www-sal.cs.uiuc.edu/~hanj/ Chapter 3, Slides: http://www-sal.cs.uiuc.edu/~hanj/bk2/