1012DM01 Data Mining

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    Data Mining

    1

    1012DM01

    MI4

    Thu 9, 10 (16:10-18:00) B216

    Introduction to Data Mining

    Min-Yuh Day

    Assistant ProfessorDept. of Information Management,Tamkang University

    http://mail. tku.edu.tw/myday/2013-02-21

    http://mail.tku.edu.tw/myday/http://mail.tku.edu.tw/myday/cindex.htmhttp://www.im.tku.edu.tw/en_index.htmlhttp://english.tku.edu.tw/index.asphttp://www.tku.edu.tw/http://www.im.tku.edu.tw/http://mail.tku.edu.tw/myday/http://mail.tku.edu.tw/myday/http://mail.im.tku.edu.tw/~myday/http://www.im.tku.edu.tw/http://www.tku.edu.tw/http://english.tku.edu.tw/index.asphttp://www.im.tku.edu.tw/en_index.htmlhttp://mail.tku.edu.tw/myday/cindex.htmhttp://mail.tku.edu.tw/myday/

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    1012(2013.02 - 2013.06)

    (Data Mining)

    (Min-Yuh Day)P (TLMXB4P) 2 (2 Credits, Elective) 9,10 (Thu 16:10-18:00) B216

    2

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    Data Mining at the

    Intersection of Many Disciplines

    Statistics

    Management Science &Information Systems

    Artificia

    lIntelligen

    ce

    Databases

    PatternRecognition

    MachineLearning

    MathematicalModeling

    DATA

    MINING

    Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 3

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    Knowledge Discovery (KDD) Process

    Data Cleaning

    Data Integration

    Databases

    Data Warehouse

    Task-relevant Data

    Selection

    Data Mining

    Pattern Evaluation

    4Source: Han & Kamber (2006)

    Data mining:

    core of knowledge discovery process

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    Data Warehouse

    Data Mining and Business Intelligence

    Increasing potentialto supportbusiness decisions End User

    BusinessAnalyst

    DataAnalyst

    DBA

    Decision

    Making

    Data PresentationVisualization Techniques

    Data Mining

    Information Discovery

    Data ExplorationStatistical Summary, Querying, and Reporting

    Data Preprocessing/Integration, Data Warehouses

    Data Sources

    Paper, Files, Web documents, Scientific experiments, Database Systems

    5Source: Han & Kamber (2006)

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    Business PressuresResponses

    Support Model

    Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 6

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    (Data Mining)

    SAS

    7

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    Course Introduction This course introduces the fundamental concepts and

    applications technology ofdata mining. Topics include

    Introduction to Data Mining

    Association Analysis Classification and Prediction

    Cluster Analysis

    Data Mining Using SAS Enterprise Miner

    Case Study and Implementation of Data Mining

    8

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    (Objective)

    Understand and apply the fundamentalconcepts and technology ofdata mining

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    (Subject/Topics)1 102/02/21 (Introduction to Data Mining)2 102/02/28 ()

    (Peace Memorial Day) (No Classes)

    3 102/03/07 (Association Analysis)4 102/03/14 (Classification and Prediction)5 102/03/21 (Cluster Analysis)6 102/03/28 SAS

    (Data Mining Using SAS Enterprise Miner)7 102/04/04 ()

    (Children's Day, Tomb Sweeping Day)(No Classes)

    8 102/04/11 (SAS EM )

    Banking Segmentation (Cluster Analysis K-Means using SAS EM)

    (Syllabus)

    10

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    (Subject/Topics)9 102/04/18 (Midterm Presentation)10 102/04/25 11 102/05/02 (SAS EM)

    Web Site Usage Associations ( Association Analysis using SAS EM)

    12 102/05/09 (SAS EM )Enrollment Management Case Study

    (Decision Tree, Model Evaluation using SAS EM)

    13 102/05/16 (SAS EM)Credit Risk Case Study

    (Regression Analysis, Artificial Neural Network using SAS EM)

    14 102/05/23 (Term Project Presentation)15 102/05/30

    (Syllabus)

    11

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    (Slides)

    Applied Analytics Using SAS Enterprise Mining,

    Jim Georges, Jeff Thompson and Chip Wells, 2010, SAS

    Decision Support and Business Intelligence Systems,

    Ninth Edition, Efraim Turban, Ramesh Sharda, Dursun Delen,2011, Pearson

    Efraim Turban 2011

    13

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    2Team Term Project

    30 () 30 () 40

    14

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    Team Term Project

    Term Project Topics Data mining

    Web mining

    Business Intelligence 3-5

    2013.03.07 ()

    15

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    A Taxonomy for Data Mining TasksData Mining

    Prediction

    Classification

    Regression

    Clustering

    Association

    Link analysis

    Sequence analysis

    Learning Method Popular Algorithms

    Supervised

    Supervised

    Supervised

    Unsupervised

    Unsupervised

    Unsupervised

    Unsupervised

    Decision trees, ANN/MLP, SVM, Roughsets, Genetic Algorithms

    Linear/Nonlinear Regression, Regressiontrees, ANN/MLP, SVM

    Expectation Maximization, AprioryAlgorithm, Graph-based Matching

    Apriory Algorithm, FP-Growth technique

    K-means, ANN/SOM

    Outlier analysis Unsupervised K-means, Expectation Maximization (EM)

    Apriory, OneR, ZeroR, Eclat

    Classification and Regression Trees,ANN, SVM, Genetic Algorithms

    Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 16

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    The Evolution of BI Capabilities

    Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 17

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    A High-Level Architecture of BI

    Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 18

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    Mining the Social Web:

    Analyzing Data from Facebook, Twitter,

    LinkedIn, and Other Social Media Sites

    19Source:http://www.amazon.com/Mining-Social-Web-Analyzing-Facebook/dp/1449388345

    http://www.amazon.com/Mining-Social-Web-Analyzing-Facebook/dp/1449388345http://www.amazon.com/Mining-Social-Web-Analyzing-Facebook/dp/1449388345http://www.amazon.com/Mining-Social-Web-Analyzing-Facebook/dp/1449388345http://www.amazon.com/Mining-Social-Web-Analyzing-Facebook/dp/1449388345http://www.amazon.com/Mining-Social-Web-Analyzing-Facebook/dp/1449388345http://www.amazon.com/Mining-Social-Web-Analyzing-Facebook/dp/1449388345http://www.amazon.com/Mining-Social-Web-Analyzing-Facebook/dp/1449388345http://www.amazon.com/Mining-Social-Web-Analyzing-Facebook/dp/1449388345http://www.amazon.com/Mining-Social-Web-Analyzing-Facebook/dp/1449388345http://www.amazon.com/Mining-Social-Web-Analyzing-Facebook/dp/1449388345http://www.amazon.com/Mining-Social-Web-Analyzing-Facebook/dp/1449388345

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    Web Mining Success Stories

    Amazon.com, Ask.com, Scholastic.com, Website Optimization Ecosystem

    WebAnalyticsVoice of

    CustomerCustomer Experience

    Management

    Customer Interaction

    on the Web

    Analysis of Interactions Knowledge about the Holistic

    View of the Customer

    Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 20

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    Top 10 CIO Technology Priorities in 2013

    Top 10 Technology Priorities RankingAnalytics and business intelligence 1

    Mobile technologies 2

    Cloud computing (SaaS, IaaS, PaaS) 3

    Collaboration technologies (workflow) 4

    Legacy modernization 5

    IT management 6

    CRM 7Virtualization 8

    Security 9

    ERP Applications 10

    21Source: Gartner Executive Programs (January 2013)

    http://www.gartner.com/newsroom/id/2304615

    http://www.gartner.com/newsroom/id/2304615http://www.gartner.com/newsroom/id/2304615

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    Top 10 CIO Business Priorities in 2013

    Top 10 Business Priorities Ranking

    Increasing enterprise growth 1

    Delivering operational results 2

    Reducing enterprise costs 3

    Attracting and retaining new customers 4

    Improving IT applications and infrastructure 5

    Creating new products and services (innovation) 6

    Improving efficiency 7

    Attracting and retaining the workforce 8

    Implementing analytics and big data 9

    Expanding into new markets and geographies 10

    22Source: Gartner Executive Programs (January 2013)

    http://www.gartner.com/newsroom/id/2304615

    http://www.gartner.com/newsroom/id/2304615http://www.gartner.com/newsroom/id/2304615

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    Summary This course introduces the fundamental concepts and

    applications technology ofdata mining.

    Topics include

    Introduction to Data Mining

    Association Analysis

    Classification and Prediction

    Cluster Analysis

    Data Mining Using SAS Enterprise Miner

    Case Study and Implementation of Data Mining

    23

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    Contact Information

    (Min-YuhDay, Ph.D.)02-26215656 #234702-26209737i716 ()

    25137151Email [email protected]://mail.tku.edu.tw/myday/

    24

    http://www.tku.edu.tw/http://www.im.tku.edu.tw/http://mail.tku.edu.tw/myday/http://mail.tku.edu.tw/myday/http://www.im.tku.edu.tw/http://www.tku.edu.tw/

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