Discovering data mining: from concept to implementation
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Upper Saddle River, NJ
Prentice Hall
1998
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIX, 195 S. graph. Darst. |
| ISBN: | 0137439806 |
Internformat
MARC
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| 264 | 1 | |a Upper Saddle River, NJ |b Prentice Hall |c 1998 | |
| 300 | |a XIX, 195 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 650 | 7 | |a Data mining |2 gtt | |
| 650 | 7 | |a Exploration de données |2 ram | |
| 650 | 7 | |a Gestion - Informatique |2 ram | |
| 650 | 7 | |a Inteligencia artificial (computacao) |2 larpcal | |
| 650 | 7 | |a Marketing - Informatique |2 ram | |
| 650 | 7 | |a Organisation de l'entreprise - Traitement automatique des données |2 ram | |
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Wirtschaft | |
| 650 | 4 | |a Business |x Data processing | |
| 650 | 4 | |a Data mining | |
| 650 | 4 | |a Marketing |x Data processing | |
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents
Figures ix
Foreword xi
Preface xv
How This Book Is Organized xv
About the Authors xvii
Acknowledgments xviii
Comments Welcome xix
Part 1. Introduction 1
Chapter 1. Data Mining: the Basics 3
Back to the Future 3
Why Now? 5
Changed Business Environment 5
Drivers 7
Enablers 9
Toward a Definition 12
Revolution or Evolution? 14
What s So Different? 15
Not So Different 18
The Data Warehouse Connection 18
The Data Warehouse 19
The Data Mart 20
From Data Warehouse to Data Mine 20
From Data Mine to Data Warehouse 21
Data Mining and Business Intelligence 21
Where to from Here? 22
Chapter 2. Down to Business 25
Market Management Applications 26
Improved Catalog TeleSales 27
Sharper Customer Focus through Loyalty Cards 28
Turning External Influences to Advantage 29
Getting More Out of Store Promotions 30
Risk Management Applications , 30
Forecasting Financial Futures 32
Pricing Strategy in a Highly Competitive Market 33
Fraud Management Applications 34
Detecting Inappropriate Medical Treatments 34
Detecting Telephone Fraud 35
Emerging and Future Application Areas 35
When Things Go Wrong! 37
Part 2. Discovery 39
i Chapter 3. The Data Mining Process 41
I Before You Start 41
1 The Process in Overview 42
The Process in Detail 45
Business Objectives Determination 45
Data Preparation 47
Data Mining 55
Analysis of Results 56
Assimilation of Knowledge 59
Chapter 4. Face to Face with the Algorithms 61
From Application to Algorithm 61
Business Applications 62
Data Mining Operations 62
Data Mining Techniques 63
Data Mining Operations 63
Predictive Modeling 64
Database Segmentation 66
Link Analysis 68
Deviation Detection 69
Data Mining Techniques 70
Predictive Modeling: Classification. 70
Predictive Modeling: Value Prediction 76
Database Segmentation: Demographic Clustering 78
Database Segmentation: Neural Clustering 79
Link Analysis: Associations Discovery. 80
Link Analysis: Sequential Pattern Discovery 83
Link Analysis: Similar Time Sequence Discovery 85
Deviation Detection: Visualization 86
Deviation Detection: Statistics 88
Chapter 5. Evaluating Vendor Solutions 89
The Value of Technology 90
Data Mining Tools , 90
Types of Data Mining Tools 91
Data Mining Process Support 93
Technical Considerations s 97
Conclusions 99
Data Mining Applications 99
Generic Applications 99
Industry Specific Applications 100
Conclusions 100
Data Mining Services 100
Consultancy Services 100
Implementation Services 101
Education Services 101
Related Services 102
Conclusions 102
Part 3. Implementation 103
Chapter 6. Case Studies 105
Preventing Fraud and Abuse 106
, Background 106
• Business Objectives Identification 106
Data Preparation 107
Data Mining 108
Analysis of Results and Assimilation of Knowledge Ill
Summary of Findings and Benefits 113
Improving Direct Mail Responses 114
Background 114
Business Objectives Identification 114
Data Preparation 116
Data Mining 117
Analysis of Results and Assimilation of Knowledge 120
Summary of Findings and Benefits 123
Chapter 7, letting Started with Data Mining 125
Are Yoii Ready for Data Mining? 126
Challenges 127
Social Issues 127
Business Issues 128
Technical Issues 128
Planning Your Approach 129
The Business Case 132
Selecting a Candidate Application 133
In House or Outsource? 134
Assessing Vendor Solutions 136
Skills and Timescales 136
Critical Success Factors 138
Conclusions 139
Appendix A. IBM s Data Mining Solution 141
Data Mining Tools 141
Intelligent Miner 142
Intelligent Miner User Scenario 146
Companion Products 155
Intelligent Decision Server 155
Visual Warehouse 157
Parallel Visual Explorer 160
Diamond I60
Visualization Data Explorer 160
Data Mining Applications 161
Generic Applications 161
Industry Specific Applications 161
Data Mining Services 163
Consultancy Services 163
Implementation Services 163
Education Services 164
Related Services 164
Emerging Technologies 165
Text and Media Mining 165
Internet Mining 166
Miscelleanous 166
Appendix B. Special Notices 167
Appendix C. Further Reading and Resources 169
Books 169
Articles 171
Internet Resources 173
Vendor Sponsored Sites 173
Vendor Independent Sites 174
ITSO Publications 174
Redbooks on CD ROMs 174
How to Get ITSO Redbooks 175
How IBM Employees Can Get ITSO Redbooks 175
How Customers Can Get ITSO Redbooks 178
IBM Redbook Order Form 180
Glossary 181
List of Abbreviations 187
Index 189
Figures
1. Average Return on Investment from Data Warehousing Projects 9
2. New Customer Relationships Out of Reach 10
3. Data Mining Positioning 12
4. Traditional Data Analysis, Not Data Mining 17
5. Data Mining and Business Intelligence 22
6. Data Mining Application Areas 26
7. Customer Segments with Similar Characteristics 28
8. Different Investment Behavior Driven by Fiscal Treatment 29
9. Patent Reference Extracted from an Online Database 32
10. Anomaly in a Population Temperature Distribution 38
11. The Data Mining Process: Begins and Ends with the Business Objectives 42
12. Effort Required for Each Data Mining Process Step 43
13. The Data Mining Process: CVA Example 46
14. A Scatterplot: Income Plotted against Age 50
15. A Boxplot: Income Distribution for Men and Women 51
16. CVA Example: Sample Rules Output 58
17. Data Mining Applications and Their Supporting Operations and Techniques . . 62
18. Predictive Modeling 64
19. Segmentation 67
20. Pattern Matching 69
21. A Binary Decision Tree 71
22. A Neural Network 74
23. Model Effectiveness: Confusion Matrix 76
24. Linear Regression Shortcomings: Nonlinear Data 77
25. Linear Regression Shortcomings: Outliers 78
26. An Association Rule 81
27. Sequential Pattern Discovery: Transaction Database 84
28. Sequential Pattern Discovery: Customer Sequence 84
29. Sequential Pattern Discovery: Support 40% 85
30. Fraud Probability in Five Dimensional Space 87
31. Fraud and Abuse Case Study: Overview 108
32. Fraud and Abuse Case Study: Input to Neural Segmentation 109
33. Fraud and Abuse Case Study: Filtering Out the PEI Codes 109
34. Fraud and Abuse Case Study: Associations with 50% Confidence and 1% Support
110
35. Fraud and Abuse Case Study: Rules at 50% Confidence Level Ill
36. Fraud and Abuse Case Study: GP Profiles 113
37. Direct Mail Case Study: Life Cycle of a Loan Product 115
38. Direct Mail Case Study: The Business Objective 116
39. Direct Mail Case Study: Overview of Approach 118
40. Direct Mail Case Study: Predictive Model Training Phase 119
41. Direct Mail Case Study: Building the Decision Tree 120
42. Direct Mail Case Study: Lift Chart m
43. Direct Mail Case Study: Reviewing the Decision Tree 122
44. Direct Mail Case Study: Reviewing the Decision Rules 122
45. Getting Started with Data Mining 130
46. Architecture of Intelligent Miner 144
47. Intelligent Miner: User Scenario Overview 147
48. Intelligent Miner: Main Window 148
49. Specifying the Data Source/Target Files 148
50. Encode Missing Values 149
51. Intelligent Miner: Clustering Window 150
52. Three Largest Segments 152
53. Zoom In on Largest Segment (33%) 153
54. Zoom In on Commute Distance 154
55. Statistics for Largest Segment 155
56. IDS: the Capsule Concept 156
57. IDS and Intelligent Miner 157
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| spelling | Discovering data mining from concept to implementation Peter Cabena ... Upper Saddle River, NJ Prentice Hall 1998 XIX, 195 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Data mining gtt Exploration de données ram Gestion - Informatique ram Inteligencia artificial (computacao) larpcal Marketing - Informatique ram Organisation de l'entreprise - Traitement automatique des données ram Datenverarbeitung Wirtschaft Business Data processing Data mining Marketing Data processing Data Mining (DE-588)4428654-5 gnd rswk-swf Methode (DE-588)4038971-6 gnd rswk-swf Data Mining (DE-588)4428654-5 s Methode (DE-588)4038971-6 s DE-604 Cabena, Peter Sonstige oth HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008353457&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Discovering data mining from concept to implementation Data mining gtt Exploration de données ram Gestion - Informatique ram Inteligencia artificial (computacao) larpcal Marketing - Informatique ram Organisation de l'entreprise - Traitement automatique des données ram Datenverarbeitung Wirtschaft Business Data processing Data mining Marketing Data processing Data Mining (DE-588)4428654-5 gnd Methode (DE-588)4038971-6 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4038971-6 |
| title | Discovering data mining from concept to implementation |
| title_auth | Discovering data mining from concept to implementation |
| title_exact_search | Discovering data mining from concept to implementation |
| title_full | Discovering data mining from concept to implementation Peter Cabena ... |
| title_fullStr | Discovering data mining from concept to implementation Peter Cabena ... |
| title_full_unstemmed | Discovering data mining from concept to implementation Peter Cabena ... |
| title_short | Discovering data mining |
| title_sort | discovering data mining from concept to implementation |
| title_sub | from concept to implementation |
| topic | Data mining gtt Exploration de données ram Gestion - Informatique ram Inteligencia artificial (computacao) larpcal Marketing - Informatique ram Organisation de l'entreprise - Traitement automatique des données ram Datenverarbeitung Wirtschaft Business Data processing Data mining Marketing Data processing Data Mining (DE-588)4428654-5 gnd Methode (DE-588)4038971-6 gnd |
| topic_facet | Data mining Exploration de données Gestion - Informatique Inteligencia artificial (computacao) Marketing - Informatique Organisation de l'entreprise - Traitement automatique des données Datenverarbeitung Wirtschaft Business Data processing Marketing Data processing Data Mining Methode |
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