呵呵，有用就好，欢迎下载。
181# chinanovel

顶,支持中,谢谢了

欢迎下载，绝对是好书哦。
184# jkz10

有需要的同志加油下载哦，觉得好的继续回帖，呵呵，谢谢。

谢谢分享！

非常感谢！！！

谢谢楼主，很有用

这么好的东西，顶

谢谢啊。。。。。。。。。

那就看看吧……
本文来自: 人大经济论坛 数据挖掘中心 版，详细出处参考：http://www.pinggu.org/bbs/viewth ... &from^^uid=332294

好厚的一本书啊，

先顶一个，其实下了很多书了，可是真正看进去的很少

不错不错，顶楼主！

谢谢楼主的慷慨！

努力学习，谢谢楼主分享

十分感谢！

最新的么？看看再说

多谢了，lz

有道理，谢谢楼主分享心得。

谢谢，是本好书！

太好了！谢谢

恩。谢谢了~学习中

出个目录就更好了，谢谢

谢谢分享！

书的质量可是超级好的。
好书大家要顶起来啊。

内容可是相当的丰富全面啊，欢迎下载。。。
Table of Contents
Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv
Part 1 Data Mining and Knowledge Discovery Process 1
Chapter 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1. What is Data Mining? ...................................................................................... 3
2. How does Data Mining Differ from Other Approaches?................................ 5
3. Summary and Bibliographical Notes ............................................................... 6
4. Exercises ........................................................................................................... 7
Chapter 2. The Knowledge Discovery Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1. Introduction....................................................................................................... 9
2. What is the Knowledge Discovery Process? ................................................... 10
3. Knowledge Discovery Process Models............................................................ 11
4. Research Issues................................................................................................. 19
5. Summary and Bibliographical Notes ............................................................... 20
6. Exercises ........................................................................................................... 24
Part 2 Data Understanding 25
Chapter 3. Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
1. Introduction....................................................................................................... 27
2. Attributes, Data Sets, and Data Storage........................................................... 27
3. Issues Concerning the Amount and Quality of Data....................................... 37
4. Summary and Bibliographical Notes ............................................................... 44
5. Exercises ........................................................................................................... 46
Chapter 4. Concepts of Learning, Classification, and Regression . . . . . . . . . . . . . . . . . . . . . . . 49
1. Introductory Comments.................................................................................... 49
2. Classification..................................................................................................... 55
3. Summary and Bibliographical Notes ............................................................... 65
4. Exercises ........................................................................................................... 66
Chapter 5. Knowledge Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
1. Data Representation and their Categories: General Insights........................... 69
2. Categories of Knowledge Representation........................................................ 71
3. Granularity of Data and Knowledge Representation Schemes ....................... 76
4. Sets and Interval Analysis................................................................................ 77
5. Fuzzy Sets as Human-Centric Information Granules ...................................... 78
vii
viii Table of Contents
6. Shadowed Sets .................................................................................................. 82
7. Rough Sets ........................................................................................................ 84
8. Characterization of Knowledge Representation Schemes ............................... 86
9. Levels of Granularity and Perception Perspectives ......................................... 87
10. The Concept of Granularity in Rules............................................................... 88
11. Summary and Bibliographical Notes ............................................................... 89
12. Exercises ........................................................................................................... 90
Part 3 Data Preprocessing 93
Chapter 6. Databases, Data Warehouses, and OLAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
1. Introduction....................................................................................................... 95
2. Database Management Systems and SQL ....................................................... 95
3. Data Warehouses ..............................................................................................106
4. On-Line Analytical Processing (OLAP) ..........................................................116
5. Data Warehouses and OLAP for Data Mining................................................127
6. Summary and Bibliographical Notes ...............................................................128
7. Exercises ...........................................................................................................130
Chapter 7. Feature Extraction and Selection Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
1. Introduction.......................................................................................................133
2. Feature Extraction.............................................................................................133
3. Feature Selection ..............................................................................................207
4. Summary and Bibliographical Notes ...............................................................228
5. Exercises ...........................................................................................................230
Chapter 8. Discretization Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235
1. Why Discretize Data Attributes? .....................................................................235
2. Unsupervised Discretization Algorithms .........................................................237
3. Supervised Discretization Algorithms..............................................................237
4. Summary and Bibliographical Notes ...............................................................253
5. Exercises ...........................................................................................................254
Part 4 Data Mining: Methods for Constructing Data Models 255

204# yuanwenqun

Chapter 9. Unsupervised Learning: Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .257
1. From Data to Information Granules or Clusters..............................................257
2. Categories of Clustering Algorithms ...............................................................258
3. Similarity Measures..........................................................................................258
4. Hierarchical Clustering.....................................................................................260
5. Objective Function-Based Clustering ..............................................................263
6. Grid - Based Clustering....................................................................................272
7. Self-Organizing Feature Maps .........................................................................274
8. Clustering and Vector Quantization.................................................................279
9. Cluster Validity.................................................................................................280
10. Random Sampling and Clustering as a Mechanism
of Dealing with Large Datasets........................................................................284
11. Summary and Biographical Notes ...................................................................286
12. Exercises ...........................................................................................................287
Chapter 10. Unsupervised Learning: Association Rules. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .289
1. Introduction.......................................................................................................289
2. Association Rules and Transactional Data ......................................................290
3. Mining Single Dimensional, Single-Level Boolean Association Rules..........295
Table of Contents ix
4. Mining Other Types of Association Rules ......................................................301
5. Summary and Bibliographical Notes ...............................................................304
6. Exercises ...........................................................................................................305
Chapter 11. Supervised Learning: Statistical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307
1. Bayesian Methods.............................................................................................307
2. Regression.........................................................................................................346
3. Summary and Bibliographical Notes ...............................................................375
4. Exercises ...........................................................................................................376
Chapter 12. Supervised Learning: Decision Trees, Rule Algorithms, and Their Hybrids . . . 381
1. What is Inductive Machine Learning?.............................................................381
2. Decision Trees ..................................................................................................388
3. Rule Algorithms ...............................................................................................393
4. Hybrid Algorithms............................................................................................399
5. Summary and Bibliographical Notes ...............................................................416
6. Exercises ...........................................................................................................416
Chapter 13. Supervised Learning: Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .419
1. Introduction.......................................................................................................419
2. Biological Neurons and their Models ..............................................................420
3. Learning Rules..................................................................................................428
4. Neural Network Topologies .............................................................................431
5. Radial Basis Function Neural Networks..........................................................431
6. Summary and Bibliographical Notes ...............................................................449
7. Exercises ...........................................................................................................450
Chapter 14. Text Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453
1. Introduction.......................................................................................................453
2. Information Retrieval Systems.........................................................................454
3. Improving Information Retrieval Systems.......................................................462
4. Summary and Bibliographical Notes ...............................................................464
5. Exercises ...........................................................................................................465
Part 5 Data Models Assessment 467
Chapter 15. Assessment of Data Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469
1. Introduction.......................................................................................................469
2. Models, their Selection, and their Assessment ................................................470
3. Simple Split and Cross-Validation...................................................................473
4. Bootstrap ...........................................................................................................474
5. Occam’s Razor Heuristic..................................................................................474
6. Minimum Description Length Principle ..........................................................475
7. Akaike’s Information Criterion and Bayesian Information Criterion .............476
8. Sensitivity, Specificity, and ROC Analyses ....................................................477
9. Interestingness Criteria .....................................................................................484
10. Summary and Bibliographical Notes ...............................................................485
11. Exercises ...........................................................................................................486
Part 6 Data Security and Privacy Issues 487
Chapter 16. Data Security, Privacy and Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .489
1. Privacy in Data Mining ....................................................................................489
2. Privacy Versus Levels of Information Granularity .........................................490
x Table of Contents
3. Distributed Data Mining...................................................................................491
4. Collaborative Clustering...................................................................................492
5. The Development of the Horizontal Model of Collaboration .........................494
6. Dealing with Different Levels of Granularity
in the Collaboration Process.............................................................................498
7. Summary and Biographical Notes ...................................................................499
8. Exercises ...........................................................................................................501
Part 7 Overview of Key Mathematical Concepts 503
Appendix A. Linear Algebra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505
1. Vectors ..............................................................................................................505
2. Matrices.............................................................................................................519
3. Linear Transformation......................................................................................540
Appendix B. Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .547
1. Basic Concepts .................................................................................................547
2. Probability Laws...............................................................................................548
3. Probability Axioms...........................................................................................549
4. Defining Events With Set–Theoretic Operations ............................................549
5. Conditional Probability.....................................................................................551
6. Multiplicative Rule of Probability ...................................................................552
7. Random Variables ............................................................................................553
8. Probability Distribution ....................................................................................555

Appendix C. Lines and Planes in Space. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .567
1. Lines on Plane ..................................................................................................567
2. Lines and Planes in a Space.............................................................................569
3. Planes ................................................................................................................572
4. Hyperplanes ......................................................................................................575
Appendix D. Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579
1. Set Definition and Notations............................................................................579
2. Types of Sets ....................................................................................................581
3. Set Relations .....................................................................................................585
4. Set Operations...................................................................................................587
5. Set Algebra .......................................................................................................590
6. Cartesian Product of Sets .................................................................................592
7. Partition of a Nonempty Set.............................................................................596
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597

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