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Introduction to Data Mining and its Applications

of: S. Sumathi, S. N. Sivanandam

Springer-Verlag, 2006

ISBN: 9783540343516 , 851 Pages

Format: PDF, Read online

Copy protection: DRM

Windows PC,Mac OSX Apple iPad, Android Tablet PC's Read Online for: Windows PC,Mac OSX,Linux

Price: 123,95 EUR



More of the content

Introduction to Data Mining and its Applications


 

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