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这本书是教授那要来的,网上只找到了扫描版,而这个pdf不是扫描的,附书签.
书名:<Decision trees for business intelligence and data mining --using SAS Enterprise Miner>
作者: Barry De Ville



--- SAS最贵的软件: SAS Enterprise Miner

---regression外,工业界和商业建模中最常见的工具: Decision Tree

应用性很强的一本书, 对Enterprise Miner和数据挖掘有兴趣的筒子们可不要错过了!
[Barry_De_Ville]_Decision_Trees_for_Business_Intel(BookFi.org).pdf (2.07 MB, 需要: 2 个论坛币)

2013-1-17 14:14:20 上传

非扫描版,便于做笔记和标记
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关键词：英文原版书 英文原版 原版书 Enterprise regression 英文原版 建模 SAS Tree

谢谢分享啊！

谢谢分享！我下载了，分享一下目录吧，方便感兴趣的了解更多。
很快速的浏览了一遍，是一本好书，结合SAS EM讲的，几乎没有SAS代码，理论性很强，相信有时间研究的朋友一定会有很大的收获~~

Contents
Preface ................................................................................................ vii
Acknowledgments ............................................................................... xi
Chapter 1  Decision Trees—What Are They? .....................1
Introduction ..........................................................................................1
Using Decision Trees with Other Modeling Approaches  ...................5
Why Are Decision Trees So Useful?  ...................................................8
Level of Measurement  .......................................................................11
Chapter 2  Descriptive, Predictive, and Explanatory  
                 Analyses.........................................................17
Introduction .................................................................................................. 18
The Importance of Showing Context .................................................19
Antecedents ................................................................................21
Intervening Factors .....................................................................22
A Classic Study and Illustration of the Need to
Understand Context ...........................................................................23
The Effect of Context..........................................................................25
How Do Misleading Results Appear? ................................................26
Automatic Interaction Detection  ...............................................28
The Role of Validation and Statistics in Growing Decision Trees ....34
The Application of Statistical Knowledge to Growing  
Decision Trees ....................................................................................36
Significance Tests.......................................................................36
The Role of Statistics in CHAID..................................................37
Validation to Determine Tree Size and Quality ..................................40
What Is Validation? .....................................................................41
Pruning ................................................................................................44
iv Contents
Machine Learning, Rule Induction, and Statistical Decision  
Trees ................................................................................................... 49
Rule Induction ............................................................................ 50
Rule Induction and the Work of Ross Quinlan .......................... 55
The Use of Multiple Trees .......................................................... 57
A Review of the Major Features of Decision Trees .......................... 58
Roots and Trees ......................................................................... 58
Branches..................................................................................... 59
Similarity Measures .................................................................... 59
Recursive Growth....................................................................... 59
Shaping the Decision Tree......................................................... 60
Deploying Decision Trees .......................................................... 60
A Brief Review of the SAS Enterprise Miner ARBORETUM  
Procedure ................................................................................ 60
Chapter 3  The Mechanics of Decision Tree  
                  Construction ................................................. 63
The Basics of Decision Trees ............................................................ 64
Step 1—Preprocess the Data for the Decision Tree Growing  
Engine ................................................................................................. 66
Step 2—Set the Input and Target Modeling Characteristics ........... 69
Targets ........................................................................................ 69
Inputs .......................................................................................... 71
Step 3—Select the Decision Tree Growth Parameters .................... 72
Step 4—Cluster and Process Each Branch-Forming Input Field .... 74
Clustering Algorithms................................................................. 78
The Kass Merge-and-Split Heuristic ......................................... 86
Dealing with Missing Data and Missing Inputs in Decision  
Trees ........................................................................................... 87
Step 5—Select the Candidate Decision Tree Branches................... 90
Step 6—Complete the Form and Content of the Final  
               Decision Tree..................................................................... 107
Contents v
Chapter 4  Business Intelligence and Decision Trees....121
Introduction.......................................................................................122
A Decision Tree Approach to Cube Construction...........................125
Multidimensional Cubes and Decision Trees Compared:  
A Small Business Example .......................................................126
Multidimensional Cubes and Decision Trees: A Side-by-
Side Comparison ......................................................................133
The Main Difference between Decision Trees and
Multidimensional Cubes ...........................................................135
Regression as a Business Tool ........................................................136
Decision Trees and Regression Compared .............................137
Chapter 5  Theoretical Issues in the Decision Tree
                 Growing Process ..........................................145
Introduction.......................................................................................146
Crafting the Decision Tree Structure for Insight and Exposition....147
Conceptual Model.....................................................................148
Predictive Issues: Accuracy, Reliability, Reproducibility,
and Performance ......................................................................155
Sample Design, Data Efficacy, and Operational Measure  
Construction..............................................................................156
Multiple Decision Trees ....................................................................159
Advantages of Multiple Decision Trees ...................................160
Major Multiple Decision Tree Methods ....................................161
Multiple Random Classification Decision Trees ......................170
Chapter 6  The Integration of Decision Trees with Other
                  Data Mining Approaches .............................173
Introduction.......................................................................................174
Decision Trees in Stratified Regression...................................174
Time-Ordered Data ...................................................................176
Decision Trees in Forecasting Applications ....................................177
vi Contents
Decision Trees in Variable Selection............................................... 181
Decision Tree Results .............................................................. 183
Interactions............................................................................... 183
Cross-Contributions of Decision Trees and Other  
Approaches .............................................................................. 185
Decision Trees in Analytical Model Development .......................... 186
Conclusion........................................................................................ 192
Business Intelligence ............................................................... 192
Data Mining .............................................................................. 193
Glossary........................................................................ 195
References ................................................................... 211
Index............................................................................. 215

支持，先收藏了。 。谢谢 了。

谢谢。

下载。谢谢。

顶一个

不知道适合什么水平的人看，我是还没有入门的菜鸟

子鹿 发表于 2013-1-18 10:27
不知道适合什么水平的人看，我是还没有入门的菜鸟

O(∩_∩)O~  不必自谦 可以看懂的

谢谢分享