Java Data Mining: Strategy, Standard, and Practice (eBook)

Front Cover 1
Java Data Mining: Strategy, Standard, and Practice 4
Copyright Page 5
Contents 8
Preface 18
Guide to Readers 22
Part I : Strategy 26
Chapter 1. Overview of Data Mining 28
1.1 Why Data Mining Is Relevant Today? 29
1.2 Introducing Data Mining 31
1.3 The Value of Data Mining 45
1.4 Summary 48
References 49
Chapter 2. Solving Problems in Industry 50
2.1 Cross-Industry Data Mining Solutions 51
2.2 Data Mining in Industries 66
2.3 Summary 72
References 72
Chapter 3. Data Mining Process 76
3.1 A Standardized Data Mining Process 77
3.2 A More Detailed View of Data Analysis and Preparation 85
3.3 Data Mining Modeling, Analysis, and Scoring Processes 95
3.4 The Role of Databases and Data Warehouses in Data Mining 99
3.5 Data Mining in Enterprise Software Architectures 100
3.6 Advances in Automated Data Mining 106
3.7 Summary 107
References 108
Chapter 4. Mining Functions and Algorithms 110
4.1 Data Mining Functions 111
4.2 Classification 113
4.3 Regression 114
4.4 Attribute Importance 116
4.5 Association 118
4.6 Clustering 122
4.7 Summary 125
References 126
Chapter 5. JDM Strategy 128
5.1 What Is the JDM Strategy? 129
5.2 Role of Standards 135
5.3 Summary 139
References 139
Chapter 6. Getting Started 142
6.1 Business Understanding 143
6.2 Data Understanding 144
6.3 Data Preparation 146
6.4 Modeling 148
6.5 Evaluation 152
6.6 Deployment 152
6.7 Summary 154
References 154
Part II : Standards 156
Chapter 7. Java Data Mining Concepts 158
7.1 Classification Problem 159
7.2 Regression Problem 182
7.3 Attribute Importance 185
7.4 Association Rules Problem 187
7.5 Clustering Problem 190
7.6 Summary 195
References 195
Chapter 8. Design of the JDM API 198
8.1 Object Modeling of Data Mining Concepts 199
8.2 Modular Packages 212
8.3 Connection Architecture 213
8.4 Object Factories 215
8.5 Uniform Resource Identifiers for Datasets 217
8.6 Enumerated Types 217
8.7 Exceptions 219
8.8 Discovering DME Capabilities 221
8.9 Summary 222
References 222
Chapter 9. Using the JDM API 224
9.1 Connection Interfaces 225
9.2 Using JDM Enumerations 238
9.3 Using Data Specification Interfaces 239
9.4 Using Classification Interfaces 243
9.5 Using Regression Interfaces 260
9.6 Using Attribute Importance Interfaces 265
9.7 Using Association Interfaces 268
9.8 Using Clustering Interfaces 274
9.9 Summary 281
References 282
Chapter 10. XML Schema 284
10.1 Overview 285
10.2 Schema Elements 285
10.3 Schema Types 287
10.4 Using PMML with the JDM Schema 292
10.5 Use Cases for JDM XML Schema and Documents 295
10.6 Summary 296
References 296
Chapter 11. Web Services 298
11.1 What is a Web Service? 299
11.2 Service-Oriented Architecture 302
11.3 JDM Web Service 303
11.4 Enabling JDM Web Services Using JAX-RPC 318
11.5 Summary 321
References 322
Part III : Practice 324
Chapter 12. Practical Problem Solving 326
12.1 Business Scenario 1: Targeted Marketing Campaign 327
12.2 Business Scenario 2: Understanding Key Factors 346
12.3 Business Scenario 3: Using Customer Segmentation 350
12.4 Summary 363
References 364
Chapter 13. Building Data Mining Tools Using JDM 366
13.1 Data Mining Tools 367
13.2 Administrative Console 371
13.3 User Interface to Build and Save a Model 381
13.4 User Interface to Test Model Quality 401
13.5 Summary 410
Chapter 14. Getting Started with JDM Web Services 412
14.1 A Web Service Client in PhP 412
14.2 A Web Service Client in Java 422
14.3 Summary 431
References 431
Chapter 15. Impacts on IT Infrastructure 432
15.1 What Does Data Mining Require from IT? 433
15.2 Impacts on Computing Hardware 434
15.3 Impacts on Data Storage Hardware 436
15.4 Data Access 439
15.5 Backup and Recovery 441
15.6 Scheduling 441
15.7 Workflow 442
15.8 Summary 444
References 444
Chapter 16. Vendor Implementations 446
16.1 Oracle Data Mining 446
16.2 KXEN (Knowledge Extraction Engines) 456
16.3 Guidelines for New Implementers 465
16.4 Process for New JDM Users 471
16.5 Summary 471
References 471
Part IV : Wrapping Up 474
Chapter 17. Evolution of Data Mining Standards 476
17.1 Data Mining Standards 477
17.2 Java Community Process 481
17.3 Why So Many Standards? 482
17.4 Directions for Data Mining Standards 486
17.5 Summary 488
References 489
Chapter 18. Preview of Java Data Mining 2.0 490
18.1 Transformations 491
18.2 Time Series 494
18.3 Apply for Association 496
18.4 Feature Extraction 497
18.5 Statistics 498
18.6 Multi-target Models 499
18.7 Text Mining 500
18.8 Summary 501
References 502
Chapter 19. Summary 504
Further Reading 508
Glossary 510
Index 524
About the Authors 544