Data Mining Using SAS Applications-经管之家官网！

【书名】 Data Mining Using SAS Applications
【作者】George Fernandez
【出版社】Chapman & Hall/CRC
【版本】1 edition
【出版日期】December 27, 2002
【文件格式】PDF
【文件大小】14.1MB
【页数】361
【ISBN出版号】1-58488-345-6
【资料类别】教程
【市面定价】$71.96
【扫描版还是影印版】清晰，非扫描，也非影印
【是否缺页】无
【关键词】SAS,Data Mining
【内容简介】This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the authors and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use.
Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without prior permission in writing from the publisher.
【目录】
Contents
1 Data Mining: A Gentle Introduction
1.1 Introduction
1.2 Data Mining: Why Now?
1.3 Benefits of Data Mining
1.4 Data Mining: Users
1.5 Data Mining Tools
1.6 Data Mining Steps
1.7 Problems in the Data Mining Process
1.8 SAS Software: The Leader in Data Mining
1.9 User-Friendly SAS Macros for Data Mining
1.10 Summary
References
Suggested Reading and Case Studies
2 Preparing Data for Data Mining
2.1 Introduction
2.2 Data Requirements in Data Mining
2.3 Ideal Structures of Data for Data Mining
2.4 Understanding the Measurement Scale of Variables
2.5 Entire Database vs. Representative Sample
2.6 Sampling for Data Mining
2.7 SAS Applications Used in Data Preparation
2.8 Summary
References
Suggested Reading
3 Exploratory Data Analysis
3.1 Introduction
3.2 Exploring Continuous Variables
3.3 Data Exploration: Categorical Variables
3.4 SAS Macro Applications Used in Data Exploration
3.5 Summary
References
Suggested Reading
4 Unsupervised Learning Methods
4.1 Introduction
4.2 Applications of Unsupervised Learning Methods
4.3 Principal Component Analysis
4.4 Exploratory Factor Analysis
4.5 Disjoint Cluster Analysis
4.6 Bi-Plot Display of PCA, EFA, and DCA Results
4.7 PCA and EFA Using SAS Macro FACTOR
4.8 Disjoint Cluster Analysis Using SAS Macro DISJCLUS
4.9 Summary
References
Suggested Reading
5 Supervised Learning Methods: Prediction
5.1 Introduction
5.2 Applications of Supervised Predictive Methods
5.3 Multiple Linear Regression Modeling
5.4 Binary Logistic Regression Modeling
5.5 Multiple Linear Regression Using SAS Macro REGDIAG
5.6 Lift Chart Using SAS Macro LIFT
5.7 Scoring New Regression Data Using the SAS
Macro RSCORE
5.8 Logistic Regression Using SAS Macro LOGISTIC
5.9 Scoring New Logistic Regression Data Using
the SAS Macro LSCORE
5.10 Case Study 1: Modeling Multiple Linear Regression
5.11 Case Study 2: Modeling Multiple Linear
Regression with Categorical Variables
5.12 Case Study 3: Modeling Binary Logistic Regression
5.13 Summary
References
6 Supervised Learning Methods: Classification
6.1 Introduction
6.2 Discriminant Analysis
6.3 Stepwise Discriminant Analysis
6.4 Canonical Discriminant Analysis
6.5 Discriminant Function Analysis
6.6 Applications of Discriminant Analysis
6.7 Classification Tree Based on CHAID
6.8 Applications of CHAID
6.9 Discriminant Analysis Using SAS Macro DISCRIM
6.10 Decison Tree Using SAS Macro CHAID
6.11 Case Study 1: CDA and Parametric DFA
6.12 Case Study 2: Nonparametric DFA
6.13 Case Study 3: Classification Tree Using CHAID
6.14 Summary
References
Suggested Reading
7 Emerging Technologies in Data Mining
7.1 Introduction
7.2 Data Warehousing
7.3 Artificial Neural Network Methods
7.4 Market Basket Association Analysis
7.5 SAS Software: The Leader in Data Mining
7.6 Summary
References
Further Reading
Appendix: Instructions for Using the SAS Macros
【原创书评】Data Mining Using SAS Applications，本来是每章一个文件，我把这几个文件用Adobe Acrobat 8 Professional组合成一个文件了。这本书通俗易懂，很且不用昂贵的SAS Enterprise Miner，用了很多宏，且基于SAS的标准模块：BASE, STAT, GRAPH, and IML。