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Contents
6
1 Introduction to Data Mining Principles
24
1.1 Data Mining and Knowledge Discovery
25
1.2 Data Warehousing and Data Mining - Overview
28
1.3 Summary
43
1.4 Review Questions
43
2 Data Warehousing, Data Mining, and OLAP
44
2.1 Data Mining Research Opportunities and Challenges
46
2.2 Evolving Data Mining into Solutions for Insights
58
2.3 Knowledge Extraction Through Data Mining
60
2.4 Data Warehousing and OLAP
80
2.5 Data Mining and OLAP
84
2.6 Summary
95
2.7 Review Questions
95
3 Data Marts and Data Warehouse: Information Architecture for the Millennium
98
3.1 Data Marts, Data Warehouse, and OLAP
100
3.2 Data Warehousing for Healthcare: The Greatest Weapon in your Competitive Arsenal
130
3.3 Data Warehousing in the Telecommunications Industry
135
3.4 The Telecommunications Lifecycle
145
3.5 Security Issues in Data Warehouse
152
3.6 Data Warehousing: To Buy or To Build a Fundamental Choice for Insurers
163
3.7 Summary
171
3.8 Review Questions
172
4 Evolution and Scaling of Data Mining Algorithms
174
4.1 Data-Driven Evolution of Data Mining Algorithms
175
4.2 Scaling Mining Algorithms to Large DataBases
180
4.3 Summary
186
4.4 Review Questions
187
5 Emerging Trends and Applications of Data Mining
188
5.1 Emerging Trends in Business Analytics
189
5.2 Business Applications of Data Mining
193
5.3 Emerging Scienti.c Applications in Data Mining
200
5.4 Summary
205
5.5 Review Questions
206
6 Data Mining Trends and Knowledge Discovery
208
6.1 Getting a Handle on the Problem
209
6.2 KDD and Data Mining: Background
210
6.3 Related Fields
214
6.4 Summary
217
6.5 Review Questions
217
7 Data Mining Tasks, Techniques, and Applications
218
7.1 Reality Check for Data Mining
219
7.2 Data Mining: Tasks, Techniques, and Applications
227
7.3 Summary
238
7.4 Review Questions
239
8 Data Mining: an Introduction – Case Study
240
8.1 The Data Flood
241
8.2 Data Holds Knowledge
241
8.3 Data Mining: A New Approach to Information Overload
242
8.4 Summary
252
8.5 Review Questions
252
9 Data Mining & KDD
254
9.1 Data Mining and KDD – Overview
255
9.2 Data Mining: The Two Cultures
261
9.3 Summary
264
9.4 Review Questions
264
10 Statistical Themes and Lessons for Data Mining
266
10.1 Data Mining and O.cial Statistics
267
10.2 Statistical Themes and Lessons for Data Mining
269
10.3 Summary
285
10.4 Review Questions
286
11 Theoretical Frameworks for Data Mining
288
11.1 Two Simple Approaches
289
11.2 Microeconomic View of Data Mining
291
11.3 Inductive Databases
292
11.4 Summary
293
11.5 Review Questions
293
12 Major and Privacy Issues in Data Mining and Knowledge Discovery
294
12.1 Major Issues in Data Mining
295
12.2 Privacy Issues in Knowledge Discovery and Data Mining
298
12.3 Some Privacy Issues in Knowledge Discovery: The OECD Personal Privacy Guidelines
306
12.4 Summary
313
12.5 Review Questions
314
13 Active Data Mining
316
13.1 Shape De.nitions
318
13.2 Queries
320
13.3 Triggers
322
13.4 Summary
325
13.5 Review Questions
325
14 Decomposition in Data Mining - A Case Study
326
14.1 Decomposition in the Literature
327
14.2 Typology of Decomposition in Data Mining
328
14.3 Hybrid Models
329
14.4 Knowledge Structuring
332
14.5 Rule-Structuring Model
333
14.6 Decision Tables, Maps, and Atlases
334
14.7 Summary
335
14.8 Review Questions
336
15 Data Mining System Products and Research Prototypes
338
15.1 How to Choose a Data Mining System
339
15.2 Examples of Commercial Data Mining Systems
341
15.3 Summary
342
15.4 Review Questions
343
16 Data Mining in Customer Value and Customer Relationship Management
344
16.1 Data Mining: A Concept of Customer Relationship Marketing
345
16.2 Introduction to Customer Acquisition
351
16.3 Customer Relationship Management (CRM)
358
16.4 Data Mining and Customer Value and Relationships
371
16.5 CRM: Technologies and Applications
379
16.6 Data Management in Analytical Customer Relationship Management
392
16.7 Summary
408
16.8 Review Questions
408
17 Data Mining in Business
410
17.1 Business Focus on Data Engineering
411
17.2 Data Mining for Business Problems
413
17.3 Data Mining and Business Intelligence
419
17.4 Data Mining in Business - Case Studies
422
18 Data Mining in Sales Marketing and Finance
434
18.1 Data Mining can Bring Pinpoint Accuracy to Sales
436
18.2 From Data Mining to Database Marketing
437
18.3 Data Mining for Marketing Decisions
442
18.4 Increasing Customer Value by Integrating Data Mining and Campaign Management Software
448
18.5 Completing a Solution for Market-Basket Analysis – Case Study
454
18.6 Data Mining in Finance
458
18.7 Data Mining for Financial Data Analysis
459
18.8 Summary
460
18.9 Review Questions
461
19 Banking and Commercial Applications
462
19.1 Bringing Data Mining to the Forefront of Business Intelligence in Wholesale Banking
464
19.2 Distributed Data Mining Through a Centralized Solution – A Case Study
465
19.3 Data Mining in Commercial Applications
467
19.4 Decision Support Systems – Case Study
469
19.5 Keys to the Commercial Success of Data Mining – Case Studies
475
19.6 Data Mining Supports E-Commerce
481
19.7 Data Mining for the Retail Industry
485
19.8 Business Intelligence and Retailing
486
19.9 Summary
494
19.10 Review Questions
495
20 Data Mining for Insurance
496
20.1 Insurance Underwriting: Data Mining as an Underwriting Decision Support Systems
497
20.2 Business Intelligence and Insurance – Application of Business Intelligence Tools like Data Warehousing, OLAP and Data Mining in Insurance
510
20.3 Summary
520
20.4 Review Questions
521
21 Data Mining in Biomedicine and Science
522
21.1 Applications in Medicine
524
21.2 Data Mining for Biomedical and DNA Data Analysis
525
21.3 An Unsupervised Neural Network Approach to Medical Data Mining Techniques: Case Study
527
21.4 Data Mining – Assisted Decision Support for Fever Diagnosis – Case Study
538
21.5 Data Mining and Science
543
21.6 Knowledge Discovery in Science as Opposed to Business-Case Study
545
21.7 Data Mining in a Scienti.c Environment
552
21.8 Flexible Earth Science Data Mining System Architecture
557
21.9 Summary
565
21.10 Review Questions
566
22 Text and Web Mining
568
22.1 Data Mining and the Web
570
22.2 An Overview on Web Mining
572
22.3 Text Mining
581
22.4 Discovering Web Access Patterns and Trends
586
22.5 Web Usage Mining on Proxy Servers: A Case Study
595
22.6 Text Data Mining in Biomedical Literature
604
Approach – Case Study
604
22.7 Related Work
608
22.8 Summary
611
22.9 Review Questions
612
23 Data Mining in Information Analysis and Delivery
614
23.1 Information Analysis: Overview
615
23.2 Intelligent Information Delivery – Case Study
618
23.3 A Characterization of Data Mining Technologies and Processes – Case Study
622
23.4 Summary
635
23.5 Review Questions
636
24 Data Mining in Telecommunications and Control
638
24.1 Data Mining for the Telecommunication Industry
639
24.2 Data Mining Focus Areas in Telecommunication
641
24.3 A Learning System for Decision Support in Telecommunications – Case Study
644
24.4 Knowledge Processing in Control Systems
646
24.5 Data Mining for Maintenance of Complex Systems – A Case Study
649
24.6 Summary
650
24.7 Review Questions
650
25 Data Mining in Security
652
25.1 Data Mining in Security Systems
653
25.2 Real Time Data Mining-Based Intrusion Detection Systems – Case Study
654
25.3 Summary
669
Review Questions
671
APPENDIX-I Data Mining Research Projects
672
A.1 National University of Singapore: Data Mining Research Projects
672
A.2 HP Labs Research: Software Technology Laboratory
681
A.3 CRISP-DM: An Overview
684
A.4 Data Mining SuiteTM
686
A.5 The Quest Data Mining System, IBM Almaden Research Center, CA, USA
692
A.6 The Australian National University Research Projects
699
A.7 Data Mining Research Group, Monash University Australia
705
A.8 Current Projects, University of Alabama in Huntsville, AL
711
A.9 Kensington Approach Toward Enterprise Data Mining
719
APPENDIX-II Data Mining Standards
722
II.1 Data Mining Standards
723
II.2 Developing Data Mining Application Using Data Mining Standards
742
II.3 Analysis
745
II.4 Application Examples
746
II.5 Conclusion
753
Appendix 3A Intelligent Miner
754
3A.1 Data Mining Process
754
3A.2 Interpreting the Results
756
3A.3 Overview of the Intelligent Miner Components
757
3A.4 Running Intelligent Miner Servers
757
3A.5 How the Intelligent Miner Creates Output Data
759
3A.6 Performing Common Tasks
760
3A.7 Understanding Basic Concepts
761
3A.8 Main Window Areas
761
3A.9 Conclusion
763
Appendix 3B Clementine
764
3B.1 Key Findings
764
3B.2 Background Information
765
3B.3 Product Availability
766
3B.4 Software Description
767
3B.5 Architecture
768
3B.6 Methodology
769
3B.7 Clementine Server
776
3B.8 How Clementine Server Improves Performance on Large Datasets
777
3B.9 Conclusion
781
Appendix 3C Crisp
784
3C.1 Hierarchical Breakdown
784
3C.2 Mapping Generic Models to Specialized Models
785
3C.3 The CRISP-DM Reference Model
786
3C.4 Data Understanding
792
3C.5 Data Preparation
794
3C.6 Modeling
797
3C.7 Evaluation
799
3C.8 Conclusion
800
Appendix 3D Mineset
802
3D.1 Introduction
802
3D.2 Architecture
802
3D.3 MineSet Tools for Data Mining Tasks
803
3D.4 About the Raw Data
804
3D.5 Analytical Algorithms
804
3D.6 Visualization
805
3D.7 KDD Process Management
806
3D.8 History
807
3D.9 Commercial Uses
808
3D.10 Conclusion
809
Appendix 3E Enterprise Miner
810
3E.1 Tools For Data Mining Process
810
3E.2 Why Enterprise Miner
811
3E.3 Product Overview
812
3E.4 SAS Enterprise Miner 5.2 Key Features
813
3E.5 Enterprise Miner Software
816
3E.6 Enterprise Miner Process for Data Mining
819
3E.7 Client/Server Capabilities
819
3E.8 Client/Server Requirements
819
3E.9 Conclusion
820
References
822
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