Data mining explained: a manager's guide to customer-centric business intelligence
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boston [u.a.]
Digital Press
2001
|
| Schlagworte: | |
| Online-Zugang: | Publisher description Table of contents Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references (p. 363-365) and index |
| Beschreibung: | XIX, 392 S. graph. Darst. |
| ISBN: | 1555582311 |
Internformat
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| 245 | 1 | 0 | |a Data mining explained |b a manager's guide to customer-centric business intelligence |c Rhonda Delmater and Monte Hancock |
| 264 | 1 | |a Boston [u.a.] |b Digital Press |c 2001 | |
| 300 | |a XIX, 392 S. |b graph. Darst. | ||
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Datensatz im Suchindex
| _version_ | 1804135586943991808 |
|---|---|
| adam_text | Contents
Foreword xv
Acknowledgments xix
1 Why Data Mining Is Important I
I What Is Customer Centric Data Mining? 3
I. I Customer Relationship Management 4
1.2 The Strategic Information Imperative 5
1.3 Distilling Knowledge from Data 7
1.4 Who Benefits from Data Mining 10
1.5 Past Experience Can Be Used to Predict Future Events 11
1.6 Data Mining Builds Customer Relationships 12
1.7 Data Mining Yields Customer Knowledge 14
1.8 Data Mining as Part of Your CRM Strategy
Can Enhance Your Competitive Position 17
1.9 Data Mining Is Not Magic 18
1.10 Summary 19
2 How Data Mining Can Enhance Your Services and Products 21
2.1 Improved Sales and Service 21
2.2 Customer Profiling 24
2.3 Customer Interaction Center (CIC) 27
2.4 Data Mining Can Help You Improve Your Products 28
2.5 Summary 30
viii Contents
3 Data Mining Can Solve Your Most Difficult Problems 31
3.1 What Data Mining Does 32
3.2 Data Mining Solves Four Problems 33
3.3 Business Intelligence Problems Are Difficult 35
3.4 The Data Mining Process 36
3.5 Summary 55
4 The Data Mining Process 57
4.1 Discovery and Exploitation 57
4.2 Ontologies as Models 59
4.3 Scientific Basis 59
4.4 Data Mining Methodologies 61
4.5 Conventional System Development: Waterfall Process 63
4.6 Data Mining: Rapid Prototyping 65
4.7 A Generic Data Mining Project Schedule 72
4.8 Summary 74
II Pillars of the Data Mining Framework 75
5 The Information Technology of Business Intelligence 77
5.1 Business Intelligence Tools 78
5.2 Data Resources 83
5.3 Business Intelligence Applications 88
5.4 Processing Platforms 89
5.5 Business Intelligence Philosophy 89
5.6 Summary 92
6 The Data in Data Mining 93
6.1 Meta Data 94
6.2 Representation: Quantization and Coding 94
6.3 Feature Extraction and Enhancement 96
6.4 Data Quality 97
6.5 Relevance and Independence of Features 100
6.6 Data Preparation 102
6.7 Feature Selection 102
Contents jx
6.8 Demographic and Behavioral Customer Data 104
6.9 Summary 105
7 The Mathematics of Data Mining 107
7.1 Introducing Feature Space 107
7.2 Moderate Statistics Apply I I I
7.3 Probability Distribution I 15
7.4 Standard Deviation and Z score I 17
7.5 Z score in Feature Space 120
7.6 Feature Space Computations 123
7.7 Clusters 127
7.8 Making Feature Sets for Data Mining 129
7.9 Synthesis of Features 134
7.10 Good Features 135
8 Data Mining Techniques: Knowledge Discovery 137
8.1 Knowledge Is Connections 137
8.2 Taxonomy of Knowledge Discovery Techniques 140
8.3 Cluster Analysis and Auto Clustering 141
8.4 Link Analysis 146
8.5 Visualization 149
9 Data Mining Techniques: More Knowledge Discovery 159
9.1 Rule Induction and Decision Trees 159
9.2 Ten Rules Created from the Data Files 182
9.3 Rules Created from Data File 188
9.4 Rules Created from Data File 190
10 Data Mining Techniques: Predictive Models 191
10.1 Surveying Predictive Modeling Techniques 192
10.2 Current Techniques Have the Power 193
10.3 Mathematical Basics 198
10.4 Polynomial Regression Models 199
10.5 Machine Learning and Predictive Models 209
10.6 Neural Networks (NNs) 212
x Contents
10.7 Decision Values and Decision Surfaces 212
10.8 Multi Layer Perceptrons (MLPs) 215
10.9 Training a Simple Neural Network 216
10.10 More Complex Decision Surfaces 224
III Data Mining Management 227
I I Common Reasons Data Mining Projects Fail 229
I I. I Data Mining s Seven Deadly Sins 230
I 1.2 Summary 241
12 Overcoming Obstacles 243
12.1 Correlated/Irrelevant Features 243
12.2 Diluted Information 245
12.3 Syntax and Semantics 245
12.4 Population Imbalance 247
12.5 Missing or Unreliable Ground Truth 248
12.6 Making Good Feature Sets from Bad Ones 250
12.7 Associative Feature Selection 252
12.8 Class Collisions 254
12.9 Summary 255
13 Successful Data Mining Project Management 257
13.1 Project Delivery Concept 258
13.2 Project Analysis 259
13.3 Project Staffing 260
13.4 Project Schedule 262
13.5 Summary 266
IV Data Mining in Vertical Industries 267
14 Data Mining in Practice 269
14.1 Data Mining in Practice 269
14.2 Case Studies 272
14.3 Summary 273
Contents xi
15 Data Mining in Customer Service 275
15.1 The Industry 275
15.2 Challenges in Customer Service 275
15.3 General Data Mining Applications 276
15.4 Case Study: Effective Customer Centric Marketing 276
15.5 Summary 281
16 Data Mining in Retail 283
16.1 The Industry 283
16.2 Challenges in Retail 283
16.3 General Data Mining Applications 284
16.4 Case Study: Catalog Retailer Database Marketing Program 284
16.5 Summary 287
17 Data Mining in Insurance 289
17.1 The Industry 289
17.2 Challenges 289
17.3 General Data Mining Applications 290
17.4 Case Study: Workers Compensation Liability Prediction 291
17.5 Summary 295
18 Data Mining in Financial Services 297
18.1 The Industry 297
18.2 Challenges 298
18.3 General Data Mining Applications 298
18.4 Case Study: Direct Marketing Profiling 301
18.5 Summary 305
19 Data Mining in Health Care and Medicine 307
19.1 The Industry 307
19.2 Challenges 308
19.3 General Data Mining Applications 309
19.4 Case Study: Predicting Patient Diagnosis for PVD 311
19.5 Summary 313
xii Contents
20 Data Mining in Telecommunications 315
20.1 The Industry 315
20.2 Challenges in the Telecommunications Industry 315
20.3 General Data Mining Applications 316
20.4 Case Study: Modeling Direct Marketing Response
for a Communication Service 3 16
20.5 Case Study: A Predictive Model for Telecom Credit Risk 3 19
20.6 Choosing Features for Profiling 327
20.7 Summary 328
21 Data Mining in Transportation and Logistics 329
21.1 The Industry 329
21.2 Challenges 329
21.3 General Data Mining Applications 330
21.4 Case Study: Maximizing Revenue Through Forecasting 330
21.5 Case Study: Vehicle Tracking Optimization 335
21.6 Summary 338
22 Data Mining in Energy 339
22.1 The Industry 339
22.2 Challenges Faced by the Energy Industry 339
22.3 General Data Mining Applications 339
22.4 Case Study: A Shocking Problem
Hypothetical Prototype Iterations 340
22.5 Case Study: Forecasting Energy Consumption 346
22.6 Summary 348
23 Data Mining in Government 349
23.1 The Industry 349
23.2 Challenges 349
23.3 General Data Mining Applications 350
23.4 Pattern Recognition Study 350
23.5 Summary 353
Contents xiii
A Glossary 355
B Bibliography 363
C Vendor Information 367
C. I Directory Web Sites 367
C.2 Vendor Listings 368
D Statistics 101 371
E Techniques Listed by Methodology Phase 375
E.I Problem Definition (Step I) 375
E.2 Data Evaluation (Step 2) 375
E.3 Feature Extraction and Enhancement (Step 3) 376
E.4 Prototyping/Model Development (Step 4) 377
E.5 Model Evaluation (Step 5) 378
Index 379
|
| adam_txt |
Contents
Foreword xv
Acknowledgments xix
1 Why Data Mining Is Important I
I What Is Customer Centric Data Mining? 3
I. I Customer Relationship Management 4
1.2 The Strategic Information Imperative 5
1.3 Distilling Knowledge from Data 7
1.4 Who Benefits from Data Mining 10
1.5 Past Experience Can Be Used to Predict Future Events 11
1.6 Data Mining Builds Customer Relationships 12
1.7 Data Mining Yields Customer Knowledge 14
1.8 Data Mining as Part of Your CRM Strategy
Can Enhance Your Competitive Position 17
1.9 Data Mining Is Not Magic 18
1.10 Summary 19
2 How Data Mining Can Enhance Your Services and Products 21
2.1 Improved Sales and Service 21
2.2 Customer Profiling 24
2.3 Customer Interaction Center (CIC) 27
2.4 Data Mining Can Help You Improve Your Products 28
2.5 Summary 30
viii Contents
3 Data Mining Can Solve Your Most Difficult Problems 31
3.1 What Data Mining Does 32
3.2 Data Mining Solves Four Problems 33
3.3 Business Intelligence Problems Are Difficult 35
3.4 The Data Mining Process 36
3.5 Summary 55
4 The Data Mining Process 57
4.1 Discovery and Exploitation 57
4.2 Ontologies as Models 59
4.3 Scientific Basis 59
4.4 Data Mining Methodologies 61
4.5 Conventional System Development: Waterfall Process 63
4.6 Data Mining: Rapid Prototyping 65
4.7 A Generic Data Mining Project "Schedule" 72
4.8 Summary 74
II Pillars of the Data Mining Framework 75
5 The Information Technology of Business Intelligence 77
5.1 Business Intelligence Tools 78
5.2 Data Resources 83
5.3 Business Intelligence Applications 88
5.4 Processing Platforms 89
5.5 Business Intelligence Philosophy 89
5.6 Summary 92
6 The Data in Data Mining 93
6.1 Meta Data 94
6.2 Representation: Quantization and Coding 94
6.3 Feature Extraction and Enhancement 96
6.4 Data Quality 97
6.5 Relevance and Independence of Features 100
6.6 Data Preparation 102
6.7 Feature Selection 102
Contents jx
6.8 Demographic and Behavioral Customer Data 104
6.9 Summary 105
7 The Mathematics of Data Mining 107
7.1 Introducing Feature Space 107
7.2 Moderate Statistics Apply I I I
7.3 Probability Distribution I 15
7.4 Standard Deviation and Z score I 17
7.5 Z score in Feature Space 120
7.6 Feature Space Computations 123
7.7 Clusters 127
7.8 Making Feature Sets for Data Mining 129
7.9 Synthesis of Features 134
7.10 Good Features 135
8 Data Mining Techniques: Knowledge Discovery 137
8.1 Knowledge Is Connections 137
8.2 Taxonomy of Knowledge Discovery Techniques 140
8.3 Cluster Analysis and Auto Clustering 141
8.4 Link Analysis 146
8.5 Visualization 149
9 Data Mining Techniques: More Knowledge Discovery 159
9.1 Rule Induction and Decision Trees 159
9.2 Ten Rules Created from the Data Files 182
9.3 Rules Created from Data File 188
9.4 Rules Created from Data File 190
10 Data Mining Techniques: Predictive Models 191
10.1 Surveying Predictive Modeling Techniques 192
10.2 Current Techniques Have the Power 193
10.3 Mathematical Basics 198
10.4 Polynomial Regression Models 199
10.5 Machine Learning and Predictive Models 209
10.6 Neural Networks (NNs) 212
x Contents
10.7 Decision Values and Decision Surfaces 212
10.8 Multi Layer Perceptrons (MLPs) 215
10.9 Training a Simple Neural Network 216
10.10 More Complex Decision Surfaces 224
III Data Mining Management 227
I I Common Reasons Data Mining Projects Fail 229
I I. I Data Mining's Seven Deadly Sins 230
I 1.2 Summary 241
12 Overcoming Obstacles 243
12.1 Correlated/Irrelevant Features 243
12.2 Diluted Information 245
12.3 Syntax and Semantics 245
12.4 Population Imbalance 247
12.5 Missing or Unreliable Ground Truth 248
12.6 Making Good Feature Sets from Bad Ones 250
12.7 Associative Feature Selection 252
12.8 Class Collisions 254
12.9 Summary 255
13 Successful Data Mining Project Management 257
13.1 Project Delivery Concept 258
13.2 Project Analysis 259
13.3 Project Staffing 260
13.4 Project Schedule 262
13.5 Summary 266
IV Data Mining in Vertical Industries 267
14 Data Mining in Practice 269
14.1 Data Mining in Practice 269
14.2 Case Studies 272
14.3 Summary 273
Contents xi
15 Data Mining in Customer Service 275
15.1 The Industry 275
15.2 Challenges in Customer Service 275
15.3 General Data Mining Applications 276
15.4 Case Study: Effective Customer Centric Marketing 276
15.5 Summary 281
16 Data Mining in Retail 283
16.1 The Industry 283
16.2 Challenges in Retail 283
16.3 General Data Mining Applications 284
16.4 Case Study: Catalog Retailer Database Marketing Program 284
16.5 Summary 287
17 Data Mining in Insurance 289
17.1 The Industry 289
17.2 Challenges 289
17.3 General Data Mining Applications 290
17.4 Case Study: Workers' Compensation Liability Prediction 291
17.5 Summary 295
18 Data Mining in Financial Services 297
18.1 The Industry 297
18.2 Challenges 298
18.3 General Data Mining Applications 298
18.4 Case Study: Direct Marketing Profiling 301
18.5 Summary 305
19 Data Mining in Health Care and Medicine 307
19.1 The Industry 307
19.2 Challenges 308
19.3 General Data Mining Applications 309
19.4 Case Study: Predicting Patient Diagnosis for PVD 311
19.5 Summary 313
xii Contents
20 Data Mining in Telecommunications 315
20.1 The Industry 315
20.2 Challenges in the Telecommunications Industry 315
20.3 General Data Mining Applications 316
20.4 Case Study: Modeling Direct Marketing Response
for a Communication Service 3 16
20.5 Case Study: A Predictive Model for Telecom Credit Risk 3 19
20.6 Choosing Features for Profiling 327
20.7 Summary 328
21 Data Mining in Transportation and Logistics 329
21.1 The Industry 329
21.2 Challenges 329
21.3 General Data Mining Applications 330
21.4 Case Study: Maximizing Revenue Through Forecasting 330
21.5 Case Study: Vehicle Tracking Optimization 335
21.6 Summary 338
22 Data Mining in Energy 339
22.1 The Industry 339
22.2 Challenges Faced by the Energy Industry 339
22.3 General Data Mining Applications 339
22.4 Case Study: A "Shocking" Problem
Hypothetical Prototype Iterations 340
22.5 Case Study: Forecasting Energy Consumption 346
22.6 Summary 348
23 Data Mining in Government 349
23.1 The Industry 349
23.2 Challenges 349
23.3 General Data Mining Applications 350
23.4 Pattern Recognition Study 350
23.5 Summary 353
Contents xiii
A Glossary 355
B Bibliography 363
C Vendor Information 367
C. I Directory Web Sites 367
C.2 Vendor Listings 368
D Statistics 101 371
E Techniques Listed by Methodology Phase 375
E.I Problem Definition (Step I) 375
E.2 Data Evaluation (Step 2) 375
E.3 Feature Extraction and Enhancement (Step 3) 376
E.4 Prototyping/Model Development (Step 4) 377
E.5 Model Evaluation (Step 5) 378
Index 379 |
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| index_date | 2024-07-02T15:28:13Z |
| indexdate | 2024-07-09T20:42:52Z |
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| isbn | 1555582311 |
| language | English |
| lccn | 00047511 |
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| spelling | Delmater, Rhonda Verfasser aut Data mining explained a manager's guide to customer-centric business intelligence Rhonda Delmater and Monte Hancock Boston [u.a.] Digital Press 2001 XIX, 392 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references (p. 363-365) and index Data mining Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s DE-604 Hancock, Monte Sonstige oth http://www.loc.gov/catdir/description/els031/00047511.html Publisher description http://www.loc.gov/catdir/toc/els031/00047511.html Table of contents HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014950559&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Delmater, Rhonda Data mining explained a manager's guide to customer-centric business intelligence Data mining Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4428654-5 |
| title | Data mining explained a manager's guide to customer-centric business intelligence |
| title_auth | Data mining explained a manager's guide to customer-centric business intelligence |
| title_exact_search | Data mining explained a manager's guide to customer-centric business intelligence |
| title_exact_search_txtP | Data mining explained a manager's guide to customer-centric business intelligence |
| title_full | Data mining explained a manager's guide to customer-centric business intelligence Rhonda Delmater and Monte Hancock |
| title_fullStr | Data mining explained a manager's guide to customer-centric business intelligence Rhonda Delmater and Monte Hancock |
| title_full_unstemmed | Data mining explained a manager's guide to customer-centric business intelligence Rhonda Delmater and Monte Hancock |
| title_short | Data mining explained |
| title_sort | data mining explained a manager s guide to customer centric business intelligence |
| title_sub | a manager's guide to customer-centric business intelligence |
| topic | Data mining Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Data mining Data Mining |
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