Data Mining Algorithms

Transcription

1 Data Mining Algorithms Lecture Course with Tutorials Wintersemester 2003/04 RWTH Aachen, Lehrstuhl für Informatik IX Course Organization (1) Weekly Schedule Tuesday, 13:45 15:15 h, AH VI (Lecture) Thursday, 13:45 15:15 h, AH VI (Lecture) Friday, 10:00 11:30 h, AH VI (Tutorials) Begins: Oct. 24th People Office hour: Monday, 13:00 14:00 h Dipl.-Inform. Ira Assent Dipl.-Inform. Christoph Brochhaus WS 2003/04 Data Mining Algorithmen 2

2 Course Organization (2) Credits Lecture course with tutorials (4 + 2 hrs/wk) 8 ECTS for Praktische Informatik or specialization Datenbanken und Datenexploration / Databases and Data Mining Selected exercises as homework (50% points for exam admission) Oral or written exam at the end of semester Material (Slides, Exercises, etc.) available from: DataMiningAlgorithmen/ WS 2003/04 Data Mining Algorithmen 3 Content of the Course Introduction Data warehousing and OLAP for data mining Data preprocessing Clustering Classification and prediction techniques Mining association rules in large databases Further topics WS 2003/04 Data Mining Algorithmen 4

3 Textbook / Acknowledgements The slides used in this course are modified versions of the copyrighted original slides provided by the authors of the adopted textbooks: Jiawei Han and Micheline Kamber: Data Mining Concepts and Techniques Morgan Kaufmann Publishers, Martin Ester and Jörg Sander: Knowledge Discovery in Databases Techniken und Anwendungen Springer Verlag, 2000 (in German). WS 2003/04 Data Mining Algorithmen 5 Source and History of the Slides Prof. Dr. Jiawei Han started working on this set of slides for an UCLA Extension course in February Dr. Hongjun Lu from Hong Kong Univ. of Science and Technology taught jointly with Jiawei Han a Data Mining Summer Course in Shanghai, China in July He has contributed many 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). Prof. Dr. Jörg Sander, Univ. of Alberta in Edmonton, Canada provided some more slides, particularly on clustering algorithms. They are an extension of slides used for a Data Mining course at Univ. of Munich jointly taught with Prof. Dr. Martin Ester (now teaching in Simon-Fraser-Univ. in Vancouver). WS 2003/04 Data Mining Algorithmen 6

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 WS 2003/04 Data Mining Algorithmen 7 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 (OLAP) Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases WS 2003/04 Data Mining Algorithmen 8

5 Evolution of Database Technology 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 WS 2003/04 Data Mining Algorithmen 9 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 Alternative names and their inside stories : Data mining: a misnomer? Knowledge discovery(mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging ( Ausbaggern ), information harvesting, business intelligence, etc. What is not data mining? (Deductive) query processing. Expert systems or small ML/statistical programs WS 2003/04 Data Mining Algorithmen 10

6 Why Data Mining? Potential Applications Database analysis and decision support Market analysis and management target marketing, customer relation management, market basket analysis, cross selling, market segmentation Risk analysis and management Forecasting, customer retention (Eigenbeteiligung), improved underwriting, quality control, competitive analysis Fraud detection and management Other Applications Text mining (news group, , documents) and Web analysis. Intelligent query answering WS 2003/04 Data Mining Algorithmen 11 Market Analysis and Management (1) Where are the data sources for analysis? 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 a single to a joint bank account: marriage, etc. Cross-market analysis Associations/co-relations between product sales Prediction based on the association information WS 2003/04 Data Mining Algorithmen 12

7 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 Provide summary information various multidimensional summary reports statistical summary information (data central tendency and variation of the data) WS 2003/04 Data Mining Algorithmen 13 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 WS 2003/04 Data Mining Algorithmen 14

8 Fraud Detection and Management Applications 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 (e.g., US Treasury's Financial Crimes Enforcement Network) medical insurance: detect professional patients and ring of doctors and ring of references WS 2003/04 Data Mining Algorithmen 15 Data Mining and Business Intelligence Increasing potential to support business decisions Making Decisions End User Data Presentation Visualization Techniques Data Mining Information Discovery 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 WS 2003/04 Data Mining Algorithmen 16

9 Architecture of Typical Data Mining Systems Graphical user interface Pattern evaluation Data mining engine Database or data warehouse server Data cleaning & data integration Filtering Knowledge-base Databases Data Warehouse WS 2003/04 Data Mining Algorithmen 17 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 WS 2003/04 Data Mining Algorithmen 18

10 The KDD Process Selections Projections Transformations Task-relevant Data Data Mining Patterns Visualization Evaluation Knowledge Data Cleaning Data Integration Databases/ Information Repositories Data Warehouse Data Mining: a central step in the process application of algorithms that find patterns in the data. WS 2003/04 Data Mining Algorithmen 19 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: 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 for patterns of interest Pattern evaluation and knowledge presentation visualization, transformation, removing redundant patterns, etc. Use of discovered knowledge WS 2003/04 Data Mining Algorithmen 20

11 Data Cleaning and Integration Integration of data from different sources Mapping of attribute names (e.g. C_Nr O_Id) Joining different tables (e.g. Table1 = [C_Nr, Info1] and Table2 = [O_Id, Info2] JoinedTable = [O_Id, Info1, Info2]) Data Cleaning Data Integration Data Warehouse Databases/ Information Repositories Elimination of inconsistencies Elimination of noise Computation of Missing Values (if necessary and possible) Fill in missing values by some strategy (e.g. default value, average value, or application specific computations) WS 2003/04 Data Mining Algorithmen 21 Focusing on task-relevant data Selections Selections Projections Select the relevant tuples/rows Transformations from the database tables (e.g., sales data for the year 2001) Data Warehouse Projections Select the relevant attributes/columns from the database tables (e.g., id, date amount from (Id, name, date, location, amount)) Transformations, e.g.: Normalization (e.g., age:[18, 87] n_age:[0, 100] Task-relevant Data Discretisation of numerical attributes (e.g., amount:[0, 100] d_amount:{low, medium, high} Computation of derived tuples/rows and derived attributes aggregation of sets of tuples, e.g., total amount per months new attributes, e.g., diff = sales current month sales previous month) WS 2003/04 Data Mining Algorithmen 22

12 Basic Data Mining Tasks Clustering Classification Association Rules Task-relevant Data Data Mining Concept Characterization and Discrimination Other methods Outlier detection Sequential patterns Trends and analysis of changes Methods for special data types, e.g., spatial data mining, web mining Patterns WS 2003/04 Data Mining Algorithmen 23 Clustering Class labels are unknown: Group objects into sub-groups (clusters) Similarity function (or dissimilarity fct. = distance) to measure similarity between objects Objective: maximize intra-class similarity and minimize interclass similarity Applications Customer profiling/segmentation Document or image collections Web access patterns... WS 2003/04 Data Mining Algorithmen 24

13 Classification Class labels are known for a small set of training data : Find models/functions/rules (based on attribute values of the training examples) that describe and distinguish classes predict class membership for new objects Applications a b a a a Classify gene expression values for tissue samples to predict disease type and suggest best possible treatment Automatic assignment of categories to large sets of newly observed celestial objects Predict unknown or missing values ( KDD pre-processing step)... WS 2003/04 Data Mining Algorithmen 25 a a a b b b a b b b b b a Association Rules Find frequent associations in transaction databases Frequently co-occurring items in the set of transactions (frequent itemsets): indicate correlations or causalities Rules with a minimum confidence based on frequent itemsets Applications: Market-basket analysis Cross-marketing Catalog design Also used as a basis for clustering, classification Transaction ID Items Bought 2000 A,B,C 1000 A,C 4000 A,D 5000 B,E,F Examples: buys(x, diapers ) buys(x, beers ) [support: 0.5%, confidence: 60%] major(x, CS ) ^ takes(x, DB ) grade(x, A ) [support: 1%, confidence: 75%] WS 2003/04 Data Mining Algorithmen 26

14 Generalization, Characterization, Discrimination Generalize, summarize, and contrast data characteristics Based on attribute aggregation along concept hierarchies Data cube approach (OLAP) Attribute-oriented induction approach all all region Europe... North_America country Germany... Spain Canada... Mexico city Frankfurt... Vancouver... Toronto office L. Chan... M. Wind WS 2003/04 Data Mining Algorithmen 27 Other Methods Outlier detection Find objects that do not comply with the general behavior of the data (fraud detection, rare events analysis) Trends and Evolution Analysis Sequential patterns (find re-occurring sequences of events) Regression analysis Methods for special data types, and applications e.g., Spatial data mining Web mining Bio-KDD Graphs... WS 2003/04 Data Mining Algorithmen 28

15 Evaluation and Visualization Integration of visualization and data mining data visualization data mining result visualization data mining process visualization interactive visual data mining Patterns Visualization Evaluation Knowledge Different types of 2D/3D plots, charts and diagrams are used, e.g.: Box-plots Trees X-Y-Plots Parallel coordinates... WS 2003/04 Data Mining Algorithmen 29 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. WS 2003/04 Data Mining Algorithmen 30

16 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 First generate all the patterns and then filter out the uninteresting ones. Generate only the interesting patterns mining query optimization WS 2003/04 Data Mining Algorithmen 31 Data Mining: Confluence of Multiple Disciplines Database Technology Statistics Machine Learning Data Mining Visualization Information Science Other Disciplines WS 2003/04 Data Mining Algorithmen 32

17 Data Mining: Classification 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 WS 2003/04 Data Mining Algorithmen 33 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. WS 2003/04 Data Mining Algorithmen 34

18 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 WS 2003/04 Data Mining Algorithmen 35 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 WS 2003/04 Data Mining Algorithmen 36

19 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 WS 2003/04 Data Mining Algorithmen 37 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 WS 2003/04 Data Mining Algorithmen 38

20 A Brief History of Data Mining Society 1989 IJCAI (Int l Joint Conf. on Artificial Intelligence) Workshop on Knowledge Discovery in Databases (Piatetsky-Shapiro) Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991) Workshops on Knowledge Discovery in Databases Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky- Shapiro, P. Smyth, and R. Uthurusamy, 1996) 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 conferences, and SIGKDD Explorations More conferences on data mining PAKDD, PKDD, SIAM-Data Mining, (IEEE) ICDM, etc. WS 2003/04 Data Mining Algorithmen 39 Where to Find References? Data mining and KDD (SIGKDD member CDROM): Conference proceedings: KDD, and others, such as PKDD, PAKDD, etc. Journal: Data Mining and Knowledge Discovery Database field (SIGMOD member CD ROM): 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: Machine learning, AAAI, IJCAI, etc. Journals: Machine Learning, Artificial Intelligence, etc. Statistics: Conference proceedings: Joint Stat. Meeting, etc. Journals: Annals of statistics, etc. Visualization: Conference proceedings: CHI (Comp. Human Interaction), etc. Journals: IEEE Trans. visualization and computer graphics, etc. WS 2003/04 Data Mining Algorithmen 40

21 References U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Communications of the ACM, 39:58-64, 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, AAAI/MIT Press, G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, M. Ester and J. Sander. Knowledge Discovery in Databases: Techniken und Anwendungen. Springer Verlag, 2000 (in German). M. H. Dunham. Data Mining: Introductory and Advanced Topics. Prentice Hall, D. Hand, H. Mannila, P. Smyth. Principles of Data Mining. MIT Press, WS 2003/04 Data Mining Algorithmen 41