SlideShare uma empresa Scribd logo
1 de 34
Baixar para ler offline
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
February 22, 2012              Data Mining: Concepts and Techniques   1
Where to Find the Set of 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
February 22, 2012           Data Mining: Concepts and Techniques   2
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
February 22, 2012      Data Mining: Concepts and Techniques   3
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
February 22, 2012            Data Mining: Concepts and Techniques               4
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
February 22, 2012           Data Mining: Concepts and Techniques                   5
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, information harvesting, business intelligence, etc.
     • What is not data mining?
           – (Deductive) query processing.
           – Expert systems or small ML/statistical programs
February 22, 2012            Data Mining: Concepts and Techniques            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, improved underwriting, quality
               control, competitive analysis
       – Fraud detection and management
 • Other Applications
       – Text mining (news group, email, documents) and Web analysis.
       – Intelligent query answering
February 22, 2012            Data Mining: Concepts and Techniques                7
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 single to a joint bank account: marriage, etc.
  • Cross-market analysis
        – Associations/co-relations between product sales
        – Prediction based on the association information

February 22, 2012           Data Mining: Concepts and Techniques                8
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)
February 22, 2012              Data Mining: Concepts and Techniques           9
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

February 22, 2012            Data Mining: Concepts and Techniques                10
Fraud Detection and Management (1)

  • 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 (US
          Treasury's Financial Crimes Enforcement Network)
        – medical insurance: detect professional patients and ring of doctors
          and ring of references
February 22, 2012            Data Mining: Concepts and Techniques               11
Fraud Detection and Management (2)

  • Detecting inappropriate medical treatment
        – Australian Health Insurance Commission identifies that 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.
  • Retail
        – Analysts estimate that 38% of retail shrink is due to dishonest
          employees.


February 22, 2012           Data Mining: Concepts and Techniques               12
Other Applications

   • Sports
         – 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
   • 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.
February 22, 2012           Data Mining: Concepts and Techniques              13
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


             Databases
 February 22, 2012            Data Mining: Concepts and Techniques             14
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
February 22, 2012              Data Mining: Concepts and Techniques             15
Data Mining and Business Intelligence
 Increasing potential
 to support
 business decisions                                                          End User
                                          Making
                                          Decisions

                                      Data Presentation                      Business
                                                                              Analyst
                                 Visualization Techniques
                                        Data Mining                            Data
                                     Information Discovery                   Analyst

                                      Data Exploration
                        Statistical Analysis, Querying and Reporting

                               Data Warehouses / Data Marts
                                       OLAP, MDA                                DBA
                                      Data Sources
               Paper, Files, Information Providers, Database Systems, OLTP
February 22, 2012               Data Mining: Concepts and Techniques               16
Architecture of a Typical Data
                    Mining System
                        Graphical user interface


                          Pattern evaluation

                        Data mining engine
                                                                         Knowledge-base
                             Database or data
                             warehouse server
            Data cleaning & data integration                 Filtering

                                                      Data
                        Databases                   Warehouse
February 22, 2012            Data Mining: Concepts and Techniques                   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
February 22, 2012         Data Mining: Concepts and Techniques   18
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%]
February 22, 2012       Data Mining: Concepts and Techniques   19
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
February 22, 2012            Data Mining: Concepts and Techniques                20
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
February 22, 2012               Data Mining: Concepts and Techniques                 21
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.
February 22, 2012              Data Mining: Concepts and Techniques                       22
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 general all the patterns and then filter out the uninteresting
                ones.
              • Generate only the interesting patterns—mining query optimization

February 22, 2012             Data Mining: Concepts and Techniques                 23
Data Mining: Confluence of Multiple
                                Disciplines
                     Database
                                                          Statistics
                    Technology



   Machine
   Learning
                                 Data Mining                              Visualization



              Information                                            Other
                Science                                            Disciplines

February 22, 2012           Data Mining: Concepts and Techniques                     24
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
February 22, 2012          Data Mining: Concepts and Techniques   25
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.
February 22, 2012             Data Mining: Concepts and Techniques                 26
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
February 22, 2012           Data Mining: Concepts and Techniques               27
An OLAM Architecture
Mining query                                         Mining result      Layer4
                                                                     User Interface
                         User GUI API
                                                                        Layer3
          OLAM                                         OLAP
          Engine                                       Engine        OLAP/OLAM

                          Data Cube API

                                                                        Layer2
                             MDDB
                                                                        MDDB
                                                       Meta Data

 Filtering&Integration    Database API                 Filtering
                                                                        Layer1
                           Data cleaning               Data
            Databases                                                   Data
February 22, 2012          Data
                                integration Warehouse
                          Data Mining: Concepts and Techniques        Repository
                                                                             28
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
February 22, 2012           Data Mining: Concepts and Techniques            29
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
February 22, 2012         Data Mining: Concepts and Techniques              30
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

February 22, 2012         Data Mining: Concepts and Techniques               31
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-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 mining
      – PAKDD, PKDD, SIAM-Data Mining, (IEEE) ICDM, etc.
February 22, 2012               Data Mining: Concepts and Techniques                           32
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, etc.
     – Journals: IEEE Trans. visualization and computer graphics, etc.
February 22, 2012               Data Mining: Concepts and Techniques            33
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.




February 22, 2012               Data Mining: Concepts and Techniques                             34

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Introduction to Data Mining
Introduction to Data Mining Introduction to Data Mining
Introduction to Data Mining
 
Introduction To Data Mining
Introduction To Data Mining   Introduction To Data Mining
Introduction To Data Mining
 
Introduction to Data mining
Introduction to Data miningIntroduction to Data mining
Introduction to Data mining
 
Data warehouse architecture
Data warehouse architectureData warehouse architecture
Data warehouse architecture
 
Introduction data mining
Introduction data miningIntroduction data mining
Introduction data mining
 
Data mining , Knowledge Discovery Process, Classification
Data mining , Knowledge Discovery Process, ClassificationData mining , Knowledge Discovery Process, Classification
Data mining , Knowledge Discovery Process, Classification
 
Data mining
Data miningData mining
Data mining
 
Data Mining
Data MiningData Mining
Data Mining
 
Data mining presentation.ppt
Data mining presentation.pptData mining presentation.ppt
Data mining presentation.ppt
 
Association rule mining.pptx
Association rule mining.pptxAssociation rule mining.pptx
Association rule mining.pptx
 
Data mining
Data mining Data mining
Data mining
 
Data Mining: Association Rules Basics
Data Mining: Association Rules BasicsData Mining: Association Rules Basics
Data Mining: Association Rules Basics
 
Data Preprocessing
Data PreprocessingData Preprocessing
Data Preprocessing
 
Text mining
Text miningText mining
Text mining
 
Data Mining: Data processing
Data Mining: Data processingData Mining: Data processing
Data Mining: Data processing
 
Web mining
Web miningWeb mining
Web mining
 
Data mining slides
Data mining slidesData mining slides
Data mining slides
 
Data preprocessing
Data preprocessingData preprocessing
Data preprocessing
 
Data mining tasks
Data mining tasksData mining tasks
Data mining tasks
 
Data preprocessing
Data preprocessingData preprocessing
Data preprocessing
 

Destaque

Data Mining: Data cube computation and data generalization
Data Mining: Data cube computation and data generalizationData Mining: Data cube computation and data generalization
Data Mining: Data cube computation and data generalizationDatamining Tools
 
Literary research 101
Literary research 101Literary research 101
Literary research 101macheney
 
Annotated Bibliographies and Notetaking
Annotated Bibliographies and NotetakingAnnotated Bibliographies and Notetaking
Annotated Bibliographies and NotetakingSarah Clark
 
Annotated Bibliographies 2, Fall 2015
Annotated Bibliographies 2, Fall 2015Annotated Bibliographies 2, Fall 2015
Annotated Bibliographies 2, Fall 2015Elizabeth Johns
 
Literary Research in Ayurveda
Literary Research in AyurvedaLiterary Research in Ayurveda
Literary Research in AyurvedaJSR Prasad
 
Conducting Literary Research
Conducting Literary ResearchConducting Literary Research
Conducting Literary ResearchBoxford Library
 
Data Modeling Basics
Data Modeling BasicsData Modeling Basics
Data Modeling Basicsrenuindia
 
Marekting research applications ppt
Marekting research applications pptMarekting research applications ppt
Marekting research applications pptANSHU TIWARI
 
Data Mining In Market Research
Data Mining In Market ResearchData Mining In Market Research
Data Mining In Market Researchkevinlan
 
DATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALA
DATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALADATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALA
DATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALASaikiran Panjala
 
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & KamberChapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
 
Multidimensional data models
Multidimensional data  modelsMultidimensional data  models
Multidimensional data models774474
 
Odam: Open Data, Access and Mining
Odam: Open Data, Access and MiningOdam: Open Data, Access and Mining
Odam: Open Data, Access and MiningDaniel JACOB
 
Multidimensional Database Design & Architecture
Multidimensional Database Design & ArchitectureMultidimensional Database Design & Architecture
Multidimensional Database Design & Architecturehasanshan
 
How I data mined my text message history
How I data mined my text message historyHow I data mined my text message history
How I data mined my text message historyJoe Cannatti Jr.
 

Destaque (20)

01 Data Mining: Concepts and Techniques, 2nd ed.
01 Data Mining: Concepts and Techniques, 2nd ed.01 Data Mining: Concepts and Techniques, 2nd ed.
01 Data Mining: Concepts and Techniques, 2nd ed.
 
Data Mining: Data cube computation and data generalization
Data Mining: Data cube computation and data generalizationData Mining: Data cube computation and data generalization
Data Mining: Data cube computation and data generalization
 
Data mining notes
Data mining notesData mining notes
Data mining notes
 
Literary research 101
Literary research 101Literary research 101
Literary research 101
 
Annotated Bibliographies and Notetaking
Annotated Bibliographies and NotetakingAnnotated Bibliographies and Notetaking
Annotated Bibliographies and Notetaking
 
Annotated Bibliographies 2, Fall 2015
Annotated Bibliographies 2, Fall 2015Annotated Bibliographies 2, Fall 2015
Annotated Bibliographies 2, Fall 2015
 
Literary Research in Ayurveda
Literary Research in AyurvedaLiterary Research in Ayurveda
Literary Research in Ayurveda
 
Conducting Literary Research
Conducting Literary ResearchConducting Literary Research
Conducting Literary Research
 
Lecture13 - Association Rules
Lecture13 - Association RulesLecture13 - Association Rules
Lecture13 - Association Rules
 
Data Modeling Basics
Data Modeling BasicsData Modeling Basics
Data Modeling Basics
 
Marekting research applications ppt
Marekting research applications pptMarekting research applications ppt
Marekting research applications ppt
 
Data Mining In Market Research
Data Mining In Market ResearchData Mining In Market Research
Data Mining In Market Research
 
DATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALA
DATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALADATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALA
DATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALA
 
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & KamberChapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
 
Multidimensional data models
Multidimensional data  modelsMultidimensional data  models
Multidimensional data models
 
Odam: Open Data, Access and Mining
Odam: Open Data, Access and MiningOdam: Open Data, Access and Mining
Odam: Open Data, Access and Mining
 
Multidimensional Database Design & Architecture
Multidimensional Database Design & ArchitectureMultidimensional Database Design & Architecture
Multidimensional Database Design & Architecture
 
Data mining
Data miningData mining
Data mining
 
How I data mined my text message history
How I data mined my text message historyHow I data mined my text message history
How I data mined my text message history
 
Data modelling 101
Data modelling 101Data modelling 101
Data modelling 101
 

Semelhante a Data mining (lecture 1 & 2) conecpts and techniques

Semelhante a Data mining (lecture 1 & 2) conecpts and techniques (20)

Datamininglecture
DatamininglectureDatamininglecture
Datamininglecture
 
D
DD
D
 
Introduction
IntroductionIntroduction
Introduction
 
Introduction.ppt
Introduction.pptIntroduction.ppt
Introduction.ppt
 
isd314-01
isd314-01isd314-01
isd314-01
 
Data mining 1
Data mining 1Data mining 1
Data mining 1
 
Data mining
Data miningData mining
Data mining
 
2 introductory slides
2 introductory slides2 introductory slides
2 introductory slides
 
17 cs002
17 cs00217 cs002
17 cs002
 
Data mining
Data miningData mining
Data mining
 
Unit 1
Unit 1Unit 1
Unit 1
 
lec01-IntroductionToDataMining.pptx
lec01-IntroductionToDataMining.pptxlec01-IntroductionToDataMining.pptx
lec01-IntroductionToDataMining.pptx
 
Dma unit 1
Dma unit   1Dma unit   1
Dma unit 1
 
introduction to data mining applications
introduction to data mining applicationsintroduction to data mining applications
introduction to data mining applications
 
Data Mining- Unit-I PPT (1).ppt
Data Mining- Unit-I PPT (1).pptData Mining- Unit-I PPT (1).ppt
Data Mining- Unit-I PPT (1).ppt
 
dwdm unit 1.ppt
dwdm unit 1.pptdwdm unit 1.ppt
dwdm unit 1.ppt
 
BAS 250 Lecture 1
BAS 250 Lecture 1BAS 250 Lecture 1
BAS 250 Lecture 1
 
Data Mining .pptx
Data Mining .pptxData Mining .pptx
Data Mining .pptx
 
Introduction To Data Mining
Introduction To Data MiningIntroduction To Data Mining
Introduction To Data Mining
 
DM-Unit-1-Part 1-R.pdf
DM-Unit-1-Part 1-R.pdfDM-Unit-1-Part 1-R.pdf
DM-Unit-1-Part 1-R.pdf
 

Último

Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Will Schroeder
 
NIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopNIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopBachir Benyammi
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1DianaGray10
 
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationUsing IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationIES VE
 
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAAnypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAshyamraj55
 
Introduction to Quantum Computing
Introduction to Quantum ComputingIntroduction to Quantum Computing
Introduction to Quantum ComputingGDSC PJATK
 
Cloud Revolution: Exploring the New Wave of Serverless Spatial Data
Cloud Revolution: Exploring the New Wave of Serverless Spatial DataCloud Revolution: Exploring the New Wave of Serverless Spatial Data
Cloud Revolution: Exploring the New Wave of Serverless Spatial DataSafe Software
 
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfUiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfDianaGray10
 
Linked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesLinked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesDavid Newbury
 
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UbiTrack UK
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemAsko Soukka
 
AI Fame Rush Review – Virtual Influencer Creation In Just Minutes
AI Fame Rush Review – Virtual Influencer Creation In Just MinutesAI Fame Rush Review – Virtual Influencer Creation In Just Minutes
AI Fame Rush Review – Virtual Influencer Creation In Just MinutesMd Hossain Ali
 
Digital magic. A small project for controlling smart light bulbs.
Digital magic. A small project for controlling smart light bulbs.Digital magic. A small project for controlling smart light bulbs.
Digital magic. A small project for controlling smart light bulbs.francesco barbera
 
PicPay - GenAI Finance Assistant - ChatGPT for Customer Service
PicPay - GenAI Finance Assistant - ChatGPT for Customer ServicePicPay - GenAI Finance Assistant - ChatGPT for Customer Service
PicPay - GenAI Finance Assistant - ChatGPT for Customer ServiceRenan Moreira de Oliveira
 
Nanopower In Semiconductor Industry.pdf
Nanopower  In Semiconductor Industry.pdfNanopower  In Semiconductor Industry.pdf
Nanopower In Semiconductor Industry.pdfPedro Manuel
 
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...Aggregage
 
Babel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxBabel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxYounusS2
 
Cybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptxCybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptxGDSC PJATK
 
Spring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdfSpring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdfAnna Loughnan Colquhoun
 
RAG Patterns and Vector Search in Generative AI
RAG Patterns and Vector Search in Generative AIRAG Patterns and Vector Search in Generative AI
RAG Patterns and Vector Search in Generative AIUdaiappa Ramachandran
 

Último (20)

Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
 
NIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopNIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 Workshop
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
 
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationUsing IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
 
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAAnypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
 
Introduction to Quantum Computing
Introduction to Quantum ComputingIntroduction to Quantum Computing
Introduction to Quantum Computing
 
Cloud Revolution: Exploring the New Wave of Serverless Spatial Data
Cloud Revolution: Exploring the New Wave of Serverless Spatial DataCloud Revolution: Exploring the New Wave of Serverless Spatial Data
Cloud Revolution: Exploring the New Wave of Serverless Spatial Data
 
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfUiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
 
Linked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesLinked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond Ontologies
 
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystem
 
AI Fame Rush Review – Virtual Influencer Creation In Just Minutes
AI Fame Rush Review – Virtual Influencer Creation In Just MinutesAI Fame Rush Review – Virtual Influencer Creation In Just Minutes
AI Fame Rush Review – Virtual Influencer Creation In Just Minutes
 
Digital magic. A small project for controlling smart light bulbs.
Digital magic. A small project for controlling smart light bulbs.Digital magic. A small project for controlling smart light bulbs.
Digital magic. A small project for controlling smart light bulbs.
 
PicPay - GenAI Finance Assistant - ChatGPT for Customer Service
PicPay - GenAI Finance Assistant - ChatGPT for Customer ServicePicPay - GenAI Finance Assistant - ChatGPT for Customer Service
PicPay - GenAI Finance Assistant - ChatGPT for Customer Service
 
Nanopower In Semiconductor Industry.pdf
Nanopower  In Semiconductor Industry.pdfNanopower  In Semiconductor Industry.pdf
Nanopower In Semiconductor Industry.pdf
 
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
 
Babel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxBabel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptx
 
Cybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptxCybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptx
 
Spring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdfSpring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdf
 
RAG Patterns and Vector Search in Generative AI
RAG Patterns and Vector Search in Generative AIRAG Patterns and Vector Search in Generative AI
RAG Patterns and Vector Search in Generative AI
 

Data mining (lecture 1 & 2) conecpts and techniques

  • 1. 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 February 22, 2012 Data Mining: Concepts and Techniques 1
  • 2. Where to Find the Set of 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 February 22, 2012 Data Mining: Concepts and Techniques 2
  • 3. 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 February 22, 2012 Data Mining: Concepts and Techniques 3
  • 4. 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 February 22, 2012 Data Mining: Concepts and Techniques 4
  • 5. 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 February 22, 2012 Data Mining: Concepts and Techniques 5
  • 6. 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, information harvesting, business intelligence, etc. • What is not data mining? – (Deductive) query processing. – Expert systems or small ML/statistical programs February 22, 2012 Data Mining: Concepts and Techniques 6
  • 7. 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, improved underwriting, quality control, competitive analysis – Fraud detection and management • Other Applications – Text mining (news group, email, documents) and Web analysis. – Intelligent query answering February 22, 2012 Data Mining: Concepts and Techniques 7
  • 8. 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 single to a joint bank account: marriage, etc. • Cross-market analysis – Associations/co-relations between product sales – Prediction based on the association information February 22, 2012 Data Mining: Concepts and Techniques 8
  • 9. 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) February 22, 2012 Data Mining: Concepts and Techniques 9
  • 10. 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 February 22, 2012 Data Mining: Concepts and Techniques 10
  • 11. Fraud Detection and Management (1) • 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 (US Treasury's Financial Crimes Enforcement Network) – medical insurance: detect professional patients and ring of doctors and ring of references February 22, 2012 Data Mining: Concepts and Techniques 11
  • 12. Fraud Detection and Management (2) • Detecting inappropriate medical treatment – Australian Health Insurance Commission identifies that 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. • Retail – Analysts estimate that 38% of retail shrink is due to dishonest employees. February 22, 2012 Data Mining: Concepts and Techniques 12
  • 13. Other Applications • Sports – 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 • 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. February 22, 2012 Data Mining: Concepts and Techniques 13
  • 14. 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 Databases February 22, 2012 Data Mining: Concepts and Techniques 14
  • 15. 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 February 22, 2012 Data Mining: Concepts and Techniques 15
  • 16. Data Mining and Business Intelligence Increasing potential to support business decisions End User Making Decisions Data Presentation Business Analyst Visualization Techniques Data Mining Data Information Discovery Analyst Data Exploration Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts OLAP, MDA DBA Data Sources Paper, Files, Information Providers, Database Systems, OLTP February 22, 2012 Data Mining: Concepts and Techniques 16
  • 17. Architecture of a Typical Data Mining System Graphical user interface Pattern evaluation Data mining engine Knowledge-base Database or data warehouse server Data cleaning & data integration Filtering Data Databases Warehouse February 22, 2012 Data Mining: Concepts and Techniques 17
  • 18. 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 February 22, 2012 Data Mining: Concepts and Techniques 18
  • 19. 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%] February 22, 2012 Data Mining: Concepts and Techniques 19
  • 20. 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 February 22, 2012 Data Mining: Concepts and Techniques 20
  • 21. 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 February 22, 2012 Data Mining: Concepts and Techniques 21
  • 22. 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. February 22, 2012 Data Mining: Concepts and Techniques 22
  • 23. 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 general all the patterns and then filter out the uninteresting ones. • Generate only the interesting patterns—mining query optimization February 22, 2012 Data Mining: Concepts and Techniques 23
  • 24. Data Mining: Confluence of Multiple Disciplines Database Statistics Technology Machine Learning Data Mining Visualization Information Other Science Disciplines February 22, 2012 Data Mining: Concepts and Techniques 24
  • 25. 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 February 22, 2012 Data Mining: Concepts and Techniques 25
  • 26. 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. February 22, 2012 Data Mining: Concepts and Techniques 26
  • 27. 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 February 22, 2012 Data Mining: Concepts and Techniques 27
  • 28. An OLAM Architecture Mining query Mining result Layer4 User Interface User GUI API Layer3 OLAM OLAP Engine Engine OLAP/OLAM Data Cube API Layer2 MDDB MDDB Meta Data Filtering&Integration Database API Filtering Layer1 Data cleaning Data Databases Data February 22, 2012 Data integration Warehouse Data Mining: Concepts and Techniques Repository 28
  • 29. 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 February 22, 2012 Data Mining: Concepts and Techniques 29
  • 30. 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 February 22, 2012 Data Mining: Concepts and Techniques 30
  • 31. 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 February 22, 2012 Data Mining: Concepts and Techniques 31
  • 32. 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-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 mining – PAKDD, PKDD, SIAM-Data Mining, (IEEE) ICDM, etc. February 22, 2012 Data Mining: Concepts and Techniques 32
  • 33. 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, etc. – Journals: IEEE Trans. visualization and computer graphics, etc. February 22, 2012 Data Mining: Concepts and Techniques 33
  • 34. 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. February 22, 2012 Data Mining: Concepts and Techniques 34