Data Mining Algorithms
Lecture Course with TutorialsWintersemester 2003/04
RWTH Aachen, Lehrstuhl für Informatik IX
– Data Management and Exploration –
Prof. Dr. Thomas Seidl
Data Management and ExplorationProf. Dr. Thomas Seidl
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Data Management and ExplorationProf. Dr. Thomas Seidl
Course Organization (1)
Weekly ScheduleTuesday, 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
PeopleProf. Dr. Thomas SeidlOffice hour: Monday, 13:00 – 14:00 hDipl.-Inform. Ira Assent
Dipl.-Inform. Christoph Brochhaus
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Course Organization (2)
CreditsLecture course with tutorials (4 + 2 hrs/wk)
8 ECTS for “Praktische Informatik” or specialization “Datenbankenund 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:http://www-i9.informatik.rwth-aachen.de/Lehre/DataMiningAlgorithmen/
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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
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Textbook / Acknowledgements
The slides used in this course are modified versions of the copyrightedoriginal slides provided by the authors of the adopted textbooks:
© Jiawei Han and Micheline Kamber:Data Mining – Concepts and TechniquesMorgan Kaufmann Publishers, 2000.http://www.cs.sfu.ca/~han/dmbook
© Martin Ester and Jörg Sander:Knowledge Discovery in Databases –Techniken und AnwendungenSpringer Verlag, 2000 (in German).
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Source and History of the Slides
Prof. Dr. Jiawei Han started working on this set of slides for an UCLA Extension course in February 1998.
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 1998. He has contributed many slides to it.
Some graduate students have contributed many new slides in the followingyears. 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).
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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
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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 (OLAP)
Extraction of interesting knowledge (rules, regularities,
patterns, constraints) from data in large databases
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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
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What Is Data Mining?
Data mining (knowledge discovery in databases):Extraction of interesting (non-trivial, implicit, previouslyunknown 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
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— 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, email, documents) and Web analysis.
Intelligent query answering
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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 marketingFind clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc.
Determine customer purchasing patterns over timeConversion of a single to a joint bank account: marriage, etc.
Cross-market analysisAssociations/co-relations between product salesPrediction based on the association information
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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)
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Corporate Analysis and Risk Management
Finance planning and asset evaluationcash flow analysis and predictioncontingent 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 procedureset pricing strategy in a highly competitive market
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Fraud Detection and Management
Applicationswidely used in health care, retail, credit card services, telecommunications (phone card fraud), etc.
Approachuse historical data to build models of fraudulent behavior and use data mining to help identify similar instances
Examplesauto insurance: detect a group of people who stage accidents to collect on insurancemoney 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
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Data Mining and Business Intelligence
Increasing potentialto supportbusiness decisions
End User
BusinessAnalyst
DataAnalyst
DBA
MakingDecisions
Data PresentationVisualization Techniques
Data MiningInformation Discovery
Data Exploration
OLAP, MDA
Statistical Analysis, Querying and Reporting
Data Warehouses / Data Marts
Data SourcesPaper, Files, Information Providers, Database Systems, OLTP
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Architecture of Typical Data Mining Systems
DataWarehouse
Data cleaning & data integration Filtering
Databases
Database or data warehouse server
Data mining engine
Pattern evaluation
Graphical user interface
Knowledge-base
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Data Mining: On What Kind of Data?
Relational databasesData warehousesTransactional databasesAdvanced DB and information repositories
Object-oriented and object-relational databasesSpatial databasesTime-series data and temporal dataText databases and multimedia databasesHeterogeneous and legacy databasesWWW
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The KDD Process
Databases/Information Repositories
SelectionsProjectionsTransformations
DataMining
VisualizationEvaluation
Patterns Knowledge
Data CleaningData Integration
“Data Warehouse”
Task-relevantData
Data Mining:• a central step in the process• application of algorithms that find
“patterns” in the data.
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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 selectionData cleaning and preprocessing: (may take 60% of effort!)Data reduction and transformation:
Find useful features, dimensionality/variable reduction, invariantrepresentation.
Choosing functions of data mining summarization, classification, regression, association, clustering.
Choosing the mining algorithm(s)Data mining: search for patterns of interestPattern evaluation and knowledge presentation
visualization, transformation, removing redundant patterns, etc.Use of discovered knowledge
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Data Cleaning and IntegrationIntegration 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])
Elimination of inconsistenciesElimination of noiseComputation of Missing Values (if necessary and possible)
Fill in missing values by some strategy (e.g. default value, average value, or application specific computations)
Databases/Information Repositories
Data CleaningData Integration “Data Warehouse”
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Focusing on task-relevant dataSelections
Select the relevant tuples/rowsfrom the database tables(e.g., sales data for the year 2001)
ProjectionsSelect 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]
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 monthsnew attributes, e.g., diff = sales current month – sales previous month)
SelectionsProjectionsTransformations
“Data Warehouse”
Task-relevantData
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Basic Data Mining Tasks
ClusteringClassificationAssociation RulesConcept Characterization and DiscriminationOther methods
Outlier detectionSequential patternsTrends and analysis of changesMethods for special data types, e.g., spatial data mining, web mining…
DataMining PatternsTask-relevant
Data
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Clustering
Class labels are unknown: Group objects into sub-groups (clusters)
Similarity function (or dissimilarity fct. = distance) to measure similarity between objectsObjective: “maximize” intra-class similarity and “minimize” interclass similarity
ApplicationsCustomer profiling/segmentationDocument or image collections Web access patterns. . .
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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 classespredict class membership for “new” objects
ApplicationsClassify gene expression values for tissue samples to predict disease type and suggest best possible treatmentAutomatic assignment of categories to large sets of newly observed celestial objectsPredict unknown or missing values ( KDD pre-processing step). . .
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Association Rules
Find frequent associations in transaction databasesFrequently co-occurring items in the set of transactions (frequent itemsets): indicate correlations or causalitiesRules with a minimum confidence based on frequent itemsets
Applications:Market-basket analysis Cross-marketingCatalog designAlso used as a basis for clustering, classification
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%]
Transaction ID Items Bought
2000 A,B,C
1000 A,C
4000 A,D
5000 B,E,F
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Generalization,Characterization, Discrimination
Generalize, summarize, and contrast data characteristicsBased on attribute aggregation along concept hierarchies
Data cube approach (OLAP)Attribute-oriented induction approach
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Other Methods
Outlier detectionFind objects that do not comply with the general behavior of thedata (fraud detection, rare events analysis)
Trends and Evolution AnalysisSequential patterns (find re-occurring sequences of events)Regression analysis
Methods for special data types, and applications e.g., Spatial data mining Web miningBio-KDDGraphs. . .
…
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Evaluation and Visualization
Integration of visualization and data miningdata visualizationdata mining result visualizationdata mining process visualizationinteractive visual data mining
Different types of 2D/3D plots, charts and diagrams are used, e.g.:
Box-plotsTreesX-Y-PlotsParallel coordinates. . .
VisualizationEvaluation
Patterns Knowledge
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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.
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Can We Find All and Only Interesting Patterns?
Find all the interesting patterns: CompletenessCan a data mining system find all the interesting patterns?
Association vs. classification vs. clustering
Search for only interesting patterns: OptimizationCan 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
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Data Mining:Confluence of Multiple Disciplines
Data Mining
DatabaseTechnology Statistics
OtherDisciplines
InformationScience
MachineLearning Visualization
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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
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Data Management and ExplorationProf. Dr. Thomas SeidlA Multi-Dimensional View
of Data Mining Classification
Databases to be minedRelational, transactional, object-oriented, object-relational,active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW, etc.
Knowledge to be minedCharacterization, discrimination, association, classification, clustering, trend, deviation and outlier analysis, etc.Multiple/integrated functions and mining at multiple levels
Techniques utilizedDatabase-oriented, data warehouse (OLAP), machine learning, statistics, visualization, neural network, etc.
Applications adaptedRetail, telecommunication, banking, fraud analysis, DNA mining, stockmarket analysis, Web mining, Weblog analysis, etc.
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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 dataintegration of mining and OLAP technologies
Interactive mining multi-level knowledgeNecessity of mining knowledge and patterns at different levels ofabstraction by drilling/rolling, pivoting, slicing/dicing, etc.
Integration of multiple mining functionsCharacterized classification, first clustering and then association
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Major Issues in Data Mining (1)
Mining methodology and user interactionMining different kinds of knowledge in databasesInteractive mining of knowledge at multiple levels of abstraction
Incorporation of background knowledgeData mining query languages and ad-hoc data mining
Expression and visualization of data mining resultsHandling noise and incomplete data
Pattern evaluation: the interestingness problem
Performance and scalabilityEfficiency and scalability of data mining algorithmsParallel, distributed and incremental mining methods
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Major Issues in Data Mining (2)
Issues relating to the diversity of data typesHandling relational and complex types of dataMining information from heterogeneous databases and global information systems (WWW)
Issues related to applications and social impactsApplication of discovered knowledge
Domain-specific data mining toolsIntelligent query answeringProcess control and decision making
Integration of the discovered knowledge with existing knowledge:A knowledge fusion problemProtection of data security, integrity, and privacy
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Summary
Data mining: discovering interesting patterns from large amounts ofdata
A natural evolution of database technology, in great demand, withwide applicationsA KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentationMining 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
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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)
1991-1994 Workshops on Knowledge Discovery in DatabasesAdvances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-Shapiro, 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 miningPAKDD, PKDD, SIAM-Data Mining, (IEEE) ICDM, etc.
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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.Journal: Data Mining and Knowledge Discovery
Database field (SIGMOD member CD ROM):Conference proceedings: ACM-SIGMOD, ACM-PODS, VLDB, ICDE, EDBT, DASFAAJournals: 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.
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References
U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advancesin 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 the 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.M. Ester and J. Sander. Knowledge Discovery in Databases: Techniken undAnwendungen. Springer Verlag, 2000 (in German).M. H. Dunham. Data Mining: Introductory and Advanced Topics. Prentice Hall, 2003.D. Hand, H. Mannila, P. Smyth. Principles of Data Mining. MIT Press, 2001.