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1
Data Mining:
Concepts and Techniques
2
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
3
Why Data Mining?
 The Explosive Growth of Data: from terabytes to petabytes
 Data collection and data availability

Automated data collection tools, database systems, Web,
computerized society
 Major sources of abundant data

Business: Web, e-commerce, transactions, stocks, …

Science: Remote sensing, bioinformatics, scientific simulation, …

Society and everyone: news, digital cameras,
 We are drowning in data, but starving for knowledge!
 “Necessity is the mother of invention”—Data mining—Automated analysis of
massive data sets
4
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.)
 Application-oriented DBMS (spatial, scientific, engineering, etc.)
 1990s:
 Data mining, data warehousing, multimedia databases, and Web
databases
 2000s
 Stream data management and mining
 Data mining and its applications
 Web technology (XML, data integration) and global information systems
5
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
 Alternative name
 Knowledge discovery in databases (KDD)
 Watch out: Is everything “data mining”?
 Query processing
 Expert systems or statistical programs
6
Why Data Mining?—Potential Applications
 Data analysis and decision support
 Market analysis and management

Target marketing, customer relationship
management (CRM), market basket analysis,
market segmentation
 Risk analysis and management

Forecasting, customer retention, quality control,
competitive analysis
 Fraud detection and detection of unusual patterns
(outliers)
7
Why Data Mining?—Potential Applications
 Other Applications
 Text mining (news group, email, documents) and Web
mining
 Stream data mining
 Bioinformatics and bio-data analysis
8
Market Analysis and Management
 Where does the data come from?
 Credit card transactions, discount coupons, customer
complaint calls
 Target marketing
 Find clusters of “model” customers who share the
same characteristics: interest, income level, spending
habits, etc.
 Determine customer purchasing patterns over time
9
Market Analysis and Management
 Cross-market analysis
 Associations/co-relations between product sales, &
prediction based on such association
 Customer profiling
 What types of customers buy what products
 Customer requirement analysis
 Identifying the best products for different customers
 Predict what factors will attract new customers
10
Fraud Detection & Mining Unusual Patterns
 Approaches: Clustering & model construction for frauds, outlier
analysis
 Applications: Health care, retail, credit card service,
telecomm.
 Medical insurance

Professional patients, and ring of doctors

Unnecessary or correlated screening tests
 Telecommunications:

Phone call model: destination of the call, duration, time of day
or week. Analyze patterns that deviate from an expected norm
 Retail industry

Analysts estimate that 38% of retail shrink is due to dishonest
employees
11
Other Applications
 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.
12
Data Mining: A KDD Process
 Data mining—core of
knowledge discovery
process
Data Cleaning
Data Integration
Databases
Data
Warehouse
Task-relevant Data
Selection
Data Mining
Pattern Evaluation
13
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.
 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
14
Architecture: Typical Data Mining
System
Data
Warehouse
Data cleaning & data integration Filtering
Databases
Database or data
warehouse server
Data mining engine
Pattern evaluation
Graphical user interface
Knowledge-base
15
Data Mining: On What Kinds of Data?
 Relational database
 Data warehouse
 Transactional database
 Advanced database and information repository
 Spatial and temporal data
 Time-series data
 Stream data
 Multimedia database
 Text databases & WWW
16
Data Mining Functionalities
 Concept description: Characterization and discrimination
 Generalize, summarize, and contrast data characteristics
 Association (correlation and causality)
 Diaper  Beer [0.5%, 75%]
 Classification and Prediction
 Construct models (functions) that describe and distinguish classes
or concepts for future prediction
 Presentation: decision-tree, classification rule, neural network
17
Data Mining Functionalities
 Cluster 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 analysis
 Outlier: a data object that does not comply with the general
behavior of the data
 Useful in fraud detection, rare events analysis
 Trend and evolution analysis
 Trend and deviation: regression analysis
 Sequential pattern mining, periodicity analysis
18
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 user’s belief in the data, e.g., unexpectedness,
novelty.
19
Data Mining: Confluence of Multiple
Disciplines
Data Mining
Database
Systems
Statistics
Other
Disciplines
Algorithm
Machine
Learning
Visualization
20
Data Mining: Classification
Schemes
 Different views, different classifications
 Kinds of data to be mined
 Kinds of knowledge to be discovered
 Kinds of techniques utilized
 Kinds of applications adapted
21
Multi-Dimensional View of Data Mining
 Data to be mined
 Relational, data warehouse, transactional, stream,
object-oriented/relational, active, spatial, time-series,
text, multi-media, heterogeneous, WWW
 Knowledge to be mined
 Characterization, discrimination, association,
classification, clustering, trend/deviation, outlier
analysis, etc.
 Multiple/integrated functions and mining at multiple
levels
22
Multi-Dimensional View of Data Mining
 Techniques utilized
 Database-oriented, data warehouse (OLAP), machine
learning, statistics, visualization, etc.
 Applications adapted
 Retail, telecommunication, banking, fraud analysis, bio-
data mining, stock market analysis, Web mining, etc.
23
OLAP Mining: 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
 Necessity of mining knowledge and patterns at different levels of
abstraction.
 Integration of multiple mining functions
 Characterized classification, first clustering and then association
24
Major Issues in Data Mining
 Mining methodology
 Mining different kinds of knowledge from diverse data
types, e.g., bio, stream, Web
 Performance: efficiency, effectiveness, and scalability
 Pattern evaluation: the interestingness problem
 Incorporation of background knowledge
 Handling noise and incomplete data
 Parallel, distributed and incremental mining methods
 Integration of the discovered knowledge with existing
one: knowledge fusion
25
Major Issues in Data Mining
 User interaction
 Data mining query languages and ad-hoc mining
 Expression and visualization of data mining results
 Interactive mining of knowledge at multiple levels of
abstraction
 Applications and social impacts
 Domain-specific data mining & invisible data mining
 Protection of data security, integrity, and privacy
26
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.
 Data mining systems and architectures
 Major issues in data mining
27
Where to Find References?
 More conferences on data mining
 PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc.
 Data mining and KDD
 Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc.
 Journal: Data Mining and Knowledge Discovery, KDD Explorations
 Database systems
 Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA
 Journals: ACM-TODS, IEEE-TKDE, JIIS, J. ACM, etc.
 AI & Machine Learning
 Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), etc.
 Journals: Machine Learning, Artificial Intelligence, etc.
 Statistics
 Conferences: Joint Stat. Meeting, etc.
 Journals: Annals of statistics, etc.
 Visualization
 Conference proceedings: CHI, ACM-SIGGraph, etc.
 Journals: IEEE Trans. visualization and computer graphics, etc.

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Introduction

  • 2. 2 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
  • 3. 3 Why Data Mining?  The Explosive Growth of Data: from terabytes to petabytes  Data collection and data availability  Automated data collection tools, database systems, Web, computerized society  Major sources of abundant data  Business: Web, e-commerce, transactions, stocks, …  Science: Remote sensing, bioinformatics, scientific simulation, …  Society and everyone: news, digital cameras,  We are drowning in data, but starving for knowledge!  “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets
  • 4. 4 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.)  Application-oriented DBMS (spatial, scientific, engineering, etc.)  1990s:  Data mining, data warehousing, multimedia databases, and Web databases  2000s  Stream data management and mining  Data mining and its applications  Web technology (XML, data integration) and global information systems
  • 5. 5 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  Alternative name  Knowledge discovery in databases (KDD)  Watch out: Is everything “data mining”?  Query processing  Expert systems or statistical programs
  • 6. 6 Why Data Mining?—Potential Applications  Data analysis and decision support  Market analysis and management  Target marketing, customer relationship management (CRM), market basket analysis, market segmentation  Risk analysis and management  Forecasting, customer retention, quality control, competitive analysis  Fraud detection and detection of unusual patterns (outliers)
  • 7. 7 Why Data Mining?—Potential Applications  Other Applications  Text mining (news group, email, documents) and Web mining  Stream data mining  Bioinformatics and bio-data analysis
  • 8. 8 Market Analysis and Management  Where does the data come from?  Credit card transactions, discount coupons, customer complaint calls  Target marketing  Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc.  Determine customer purchasing patterns over time
  • 9. 9 Market Analysis and Management  Cross-market analysis  Associations/co-relations between product sales, & prediction based on such association  Customer profiling  What types of customers buy what products  Customer requirement analysis  Identifying the best products for different customers  Predict what factors will attract new customers
  • 10. 10 Fraud Detection & Mining Unusual Patterns  Approaches: Clustering & model construction for frauds, outlier analysis  Applications: Health care, retail, credit card service, telecomm.  Medical insurance  Professional patients, and ring of doctors  Unnecessary or correlated screening tests  Telecommunications:  Phone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm  Retail industry  Analysts estimate that 38% of retail shrink is due to dishonest employees
  • 11. 11 Other Applications  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.
  • 12. 12 Data Mining: A KDD Process  Data mining—core of knowledge discovery process Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation
  • 13. 13 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.  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
  • 14. 14 Architecture: Typical Data Mining System Data Warehouse Data cleaning & data integration Filtering Databases Database or data warehouse server Data mining engine Pattern evaluation Graphical user interface Knowledge-base
  • 15. 15 Data Mining: On What Kinds of Data?  Relational database  Data warehouse  Transactional database  Advanced database and information repository  Spatial and temporal data  Time-series data  Stream data  Multimedia database  Text databases & WWW
  • 16. 16 Data Mining Functionalities  Concept description: Characterization and discrimination  Generalize, summarize, and contrast data characteristics  Association (correlation and causality)  Diaper  Beer [0.5%, 75%]  Classification and Prediction  Construct models (functions) that describe and distinguish classes or concepts for future prediction  Presentation: decision-tree, classification rule, neural network
  • 17. 17 Data Mining Functionalities  Cluster 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 analysis  Outlier: a data object that does not comply with the general behavior of the data  Useful in fraud detection, rare events analysis  Trend and evolution analysis  Trend and deviation: regression analysis  Sequential pattern mining, periodicity analysis
  • 18. 18 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 user’s belief in the data, e.g., unexpectedness, novelty.
  • 19. 19 Data Mining: Confluence of Multiple Disciplines Data Mining Database Systems Statistics Other Disciplines Algorithm Machine Learning Visualization
  • 20. 20 Data Mining: Classification Schemes  Different views, different classifications  Kinds of data to be mined  Kinds of knowledge to be discovered  Kinds of techniques utilized  Kinds of applications adapted
  • 21. 21 Multi-Dimensional View of Data Mining  Data to be mined  Relational, data warehouse, transactional, stream, object-oriented/relational, active, spatial, time-series, text, multi-media, heterogeneous, WWW  Knowledge to be mined  Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc.  Multiple/integrated functions and mining at multiple levels
  • 22. 22 Multi-Dimensional View of Data Mining  Techniques utilized  Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, etc.  Applications adapted  Retail, telecommunication, banking, fraud analysis, bio- data mining, stock market analysis, Web mining, etc.
  • 23. 23 OLAP Mining: 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  Necessity of mining knowledge and patterns at different levels of abstraction.  Integration of multiple mining functions  Characterized classification, first clustering and then association
  • 24. 24 Major Issues in Data Mining  Mining methodology  Mining different kinds of knowledge from diverse data types, e.g., bio, stream, Web  Performance: efficiency, effectiveness, and scalability  Pattern evaluation: the interestingness problem  Incorporation of background knowledge  Handling noise and incomplete data  Parallel, distributed and incremental mining methods  Integration of the discovered knowledge with existing one: knowledge fusion
  • 25. 25 Major Issues in Data Mining  User interaction  Data mining query languages and ad-hoc mining  Expression and visualization of data mining results  Interactive mining of knowledge at multiple levels of abstraction  Applications and social impacts  Domain-specific data mining & invisible data mining  Protection of data security, integrity, and privacy
  • 26. 26 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.  Data mining systems and architectures  Major issues in data mining
  • 27. 27 Where to Find References?  More conferences on data mining  PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc.  Data mining and KDD  Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc.  Journal: Data Mining and Knowledge Discovery, KDD Explorations  Database systems  Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA  Journals: ACM-TODS, IEEE-TKDE, JIIS, J. ACM, etc.  AI & Machine Learning  Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), etc.  Journals: Machine Learning, Artificial Intelligence, etc.  Statistics  Conferences: Joint Stat. Meeting, etc.  Journals: Annals of statistics, etc.  Visualization  Conference proceedings: CHI, ACM-SIGGraph, etc.  Journals: IEEE Trans. visualization and computer graphics, etc.