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Data mining : building competitive advantage

Author: Robert Groth
Publisher: Upper Saddle River, NJ : Prentice Hall PTR, ©2000.
Edition/Format:   Print book : EnglishView all editions and formats
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Document Type: Book
All Authors / Contributors: Robert Groth
ISBN: 0130862711 9780130862716
OCLC Number: 42040990
Description: xxvi, 299 pages : illustrations ; 25 cm + 1 computer optical disc (4 3/4 in.)
Details: System requirements: May need a PostScript viewer (.ps files) such as Solaris; a browser (.html files); Acrobat Reader (.pdf files); Microsoft PowerPoint (.ppt files); Microsoft Word (.doc files); text editor (.txt or .sql files).
Contents: Industry Focus xxii --
Hands-On Teaching Style xxiii --
Audience xxiii --
Business Professionals xxiii --
Database Administrators (DBAs) xxiv --
Marketing Analysts xxiv --
Students xxiv --
Systems Analysts and Consultants xxiv --
CD-ROM Installation Requirements xxv --
Part 1 Starting Out 1 --
1.1 What Is Data Mining? 3 --
1.2 Why Use Data Mining? 5 --
1.2.1 Examples of Using Data Mining 6 --
1.3 Case Studies of Implementing Data Mining 9 --
1.3.1 An Example of Data Mining at US WEST 9 --
1.3.2 An Example of Data Mining at Bass Export 11 --
1.3.3 A Data-Mining Example at Reuters 12 --
1.4 A Process for Successfully Deploying Data Mining for Competitive Advantage 12 --
1.4.1 Problem Definition 13 --
1.4.2 Discovery 14 --
1.4.3 Implementation 15 --
1.4.4 Taking Action 16 --
1.4.5 Monitoring the Results 17 --
1.4.6 Discussion of the Process 18 --
1.5 A Note on Privacy Issues 19 --
Chapter 2 Getting Started with Data Mining 21 --
2.1 Classification (Supervised Learning) 22 --
2.1.1 Goal 22 --
2.1.2 Subject of the Study 23 --
2.2 Clustering (Unsupervised Learning) 24 --
2.3 A Clustering Example 25 --
2.4 Visualization 26 --
2.5 Association (Market Basket) 28 --
2.5.1 The Trouble with Market-Basket Analysis 29 --
2.6 Assortment Optimization 31 --
2.6.1 Sales Volume: Variety versus Substitutability 31 --
2.6.2 Costs: The Other Half of the Story 33 --
2.7 Prediction 35 --
2.7.1 Challenger Outcomes 36 --
2.7.2 Margin of Victory 36 --
2.7.3 Conducting a Cost Benefit Analysis 36 --
2.8 Estimation 38 --
2.8.1 Examples of Estimation 39 --
Chapter 3 The Data-Mining Process 41 --
3.1 Discussion of Data-Mining Methodology 41 --
3.1.1 The SEMMA Methodology from SAS Institute 42 --
3.2 The Example 44 --
3.3 Data Preparation 46 --
3.3.1 Getting at Your Data 47 --
3.3.2 Data-Qualification Issues 50 --
3.3.3 Data-Quality Issues 50 --
3.3.4 Binning 53 --
3.3.5 Data Derivation 54 --
3.4 Defining a Study 54 --
3.4.1 Understanding Limits 55 --
3.4.2 Choosing a Good Study 56 --
3.4.3 Types of Studies 56 --
3.4.4 What Elements to Analyze? 58 --
3.4.5 Issues of Sampling 60 --
3.5 Reading the Data and Building a Model 61 --
3.5.1 On Accuracy 61 --
3.5.2 On Understandability 61 --
3.5.3 On Performance 62 --
3.6 Understanding Your Model 62 --
3.6.1 Model Summarization 63 --
3.6.2 Data Distribution 64 --
3.6.3 Validation 65 --
3.7 Prediction 67 --
3.7.1 Challenger Outcomes 68 --
3.7.2 Margin of Victory 68 --
3.7.3 Understanding Why a Prediction Is Made 69 --
Chapter 4 Data-Mining Algorithms 71 --
4.2 Decision Trees 72 --
4.2.1 How Decision Trees Work 73 --
4.2.2 Strengths and Weaknesses of Decision Trees 74 --
4.3 Genetic Algorithms 75 --
4.3.1 How Genetic Algorithms Work 75 --
4.3.2 Strengths and Weaknesses 76 --
4.4 Neural Networks 76 --
4.4.1 How It Works 77 --
4.4.2 Different Types of Models to Build --
Unsupervised Learning 78 --
4.4.3 Strengths and Weaknesses of a Model 79 --
4.5 Bayesian Belief Networks 80 --
4.5.1 How They Work 80 --
4.5.2 Strengths and Weaknesses of Bayesian Belief Networks 82 --
4.6 Statistics 83 --
4.6.1 On Discriminant Analysis 83 --
4.6.2 On Regression Modeling 83 --
4.6.3 Strengths and Weaknesses 84 --
4.7 Advanced Algorithms for Association 84 --
4.7.1 A Better Way of Discovery Associations 85 --
4.7.2 Beyond Statistical Dependence 87 --
4.7.3 Understanding Associations 88 --
4.7.4 Actionable and Effective MB Analysis 88 --
4.8 Algorithms for Assortment Optimization 90 --
4.8.1 Cost: As Easy as ABC? 93 --
4.8.2 Relevant Costs 94 --
4.8.3 Business Goals: Bringing It All Together 95 --
Chapter 5 The Data-Mining Marketplace 99 --
5.1.1 Data Warehousing is Becoming Commonplace 100 --
5.1.2 Data Mining on the Internet 100 --
5.1.3 EIS Tool Vendors Integrating Data Mining 100 --
5.1.4 Information More Accessible 101 --
5.1.5 Data-Mining Vendors Focusing More on Vertical Markets 101 --
5.2 Data-Mining Vendors 102 --
5.3 Visualization 112 --
5.3.1 Examples of Data Visualization 112 --
5.3.2 Vendor List 115 --
5.4 Useful Web Sites/Commercially Available Code 117 --
5.4.1 Data-Mining Web Sites 118 --
5.4.2 Finding Data Sets 118 --
5.4.3 Source Code 119 --
5.5 Data Sources for Mining 120 --
Part 2 A Rapid Tutorial 125 --
Chapter 6 A Look at Angoss: KnowledgeSEEKER 127 --
6.1.1 KnowledgeSTUDIO 128 --
6.1.2 KnowledgeSEEKER and Decision Trees 128 --
6.1.3 How Decision Trees Are Being Used 128 --
6.2 Data Preparation 129 --
6.3 Defining the Study 132 --
6.3.1 Defining the Goal 132 --
6.3.2 Starting Up 133 --
6.3.3 Setting the Dependent Variable 133 --
6.4 Building the Model 134 --
6.5 Understanding the Model 135 --
6.5.1 Looking at Different Splits 135 --
6.5.2 Going to a Specific Split 138 --
6.5.3 Growing the Tree 138 --
6.5.4 Forcing a Split 140 --
6.5.5 Validation 142 --
6.5.6 Defining a New Scenario for a Study 142 --
6.5.7 Growing a Tree Automatically 143 --
6.5.8 Data Distribution 144 --
6.6 Prediction 145 --
Chapter 7 A Look at RightPoint DataCruncher 149 --
7.1.1 RightPoint's Technology 150 --
7.1.2 How RightPoint Is Being Used 151 --
7.2 Data Preparation 151 --
7.3 Defining the Study 156 --
7.3.1 Defining the Goal 156 --
7.3.2 Choosing a Dependent Variable 156 --
7.3.3 Setting Up a Study 157 --
7.3.4 Starting RightPoint 158 --
7.3.5 Setting Up Data Specifications 161 --
7.4 Read Your Data/Build a Discovery Model 170 --
7.5 Understanding the Model 170 --
7.5.1 Evaluation 178 --
7.5.2 Refining the Model 180 --
7.5.3 Conducting a Cost Benefit Analysis 182 --
7.6 Perform Prediction 185 --
7.6.1 Conducting What-If Analyses 185 --
7.6.2 Conducting Batch Prediction 187 --
Part 3 Industry Focus 189 --
Chapter 8 Industry Applications of Data Mining 191 --
8.1 Data-Mining Applications in Banking and Finance 191 --
8.1.1 Stock Forecasting 192 --
8.1.2 Cross-Selling and Customer Loyalty in the Banking Industry 193 --
8.2 Data-Mining Applications in Retail 198 --
8.2.1 An Example of Data Mining for Property Valuation 201 --
8.2.2 An Example of Analyzing Customer Profitability in Retail 203 --
8.3 Data-Mining Applications in Healthcare 204 --
8.3.1 Uses of Data Visualization in the Medical Industry 205 --
8.4 Data-Mining Applications in Telecommunications 207 --
8.4.1 Types of Studies in Telecommunications 209 --
Chapter 9 Enabling Data Mining Through Data Warehouses 211 --
9.1.1 Data Acquisition 213 --
9.1.2 Data Refinement 213 --
9.1.3 Data Warehouse Design 214 --
9.1.4 Data Warehouse and DataMart Implementation 214 --
9.2 A Data-Warehouse Example in Banking and Finance 214 --
9.2.1 A Transactional Database System versus a Data Warehouse 215 --
9.2.2 The Sample Data Model 216 --
9.2.3 An Example of a Credit-Fraud Study 219 --
9.2.4 An Example of a Retention-Management Study 222 --
9.2.5 Data-Trends Analysis 227 --
9.3 A Data-Warehouse Example in Retail 227 --
9.3.1 The Sample Data Model 228 --
9.3.2 What Types of Customers are Buying Different Types of Products 230 --
9.3.3 An Example of Regional Studies and Others 233 --
9.4 A Data-Warehouse Example in Healthcare 233 --
9.4.1 The Example Data Model 233 --
9.4.2 A Look at Sample Studies in Healthcare 237 --
9.4.3 A Discussion on Adding Credit Data to Our Example 237 --
9.5 A Data-Warehouse Example in Telecommunications 238 --
9.5.1 The Sample Data Model 238 --
9.5.2 Data Collection 241 --
9.5.3 Creating the Data Set 242 --
9.5.4 An Example Study on Product/Market Share Analysis 243 --
9.5.5 An Example Study of a Regional Market Analysis 244 --
Appendix A Data-Mining Vendors 245 --
A.1 Data-Mining Players 246 --
A.2 Visualization Tools 249 --
A.3 Useful Web Sites 250 --
A.4 Information Access Providers 250 --
A.5 Data-Warehousing Vendors 252 --
Appendix B Installing Demo Software 255 --
B.1 Installing Angoss KnowledgeSEEKER Demo 255 --
B.2 Installing the RightPoint DataCruncher Demo 256.
Responsibility: Robert Groth.

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