[下载]Springer08《高级数据挖掘技术》（Advanced Data Mining Techniques）-经管之家官网！

经济学管理学金融学统计学

您当前的位置> 软件培训>>

[下载]Springer08《高级数据挖掘技术》（Advanced Data Mining Techniques）

人大经济论坛-经管之家：分享大学、考研、论文、会计、留学、数据、经济学、金融学、管理学、统计学、博弈论、统计年鉴、行业分析包括等相关资源。
经管之家是国内活跃的在线教育咨询平台!

完整电子版已上线CDA网校，累计已有10万+在读~ 教材严格按考试大纲编写，适合CDA考生备考，也适合业务及数据分析岗位的从业者提升自我。

TOP热门关键词

专题页面精选

AdvancedDataMiningTechniquesbyDavidL.Olson(Author),DursunDelen(Author)Paperback:180pagesPublisher:Springer;1edition(February1,2008)Language:EnglishBookDescriptionThisbookcoversthefundamentalconceptsof ...

坛友互助群

扫码加入各岗位、行业、专业交流群

Advanced Data Mining Techniques
by David L. Olson (Author), Dursun Delen (Author)

Paperback: 180 pages

Publisher: Springer; 1 edition (February 1, 2008)

Language: English

Book Description
This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focusses on business applications of data mining.Methods are presented with simple examples, applications are reviewed, and relativ advantages are evaluated.

Contents
Part I INTRODUCTION
1 Introduction...............................................................................................3
What is Data Mining?..........................................................................5
What is Needed to Do Data Mining.....................................................5
Business Data Mining..........................................................................7
Data Mining Tools ...............................................................................8
Summary..............................................................................................8
2 Data Mining Process.................................................................................9
CRISP-DM ..........................................................................................9
Business Understanding.............................................................11
Data Understanding ...................................................................11
Data Preparation ........................................................................12
Modeling ...................................................................................15
Evaluation..................................................................................18
Deployment................................................................................18
SEMMA.............................................................................................19
Steps in SEMMA Process..........................................................20
Example Data Mining Process Application.......................................22
Comparison of CRISP & SEMMA....................................................27
Handling Data....................................................................................28
Summary............................................................................................34
3 Memory-Based Reasoning Methods.......................................................39
Matching ............................................................................................40
Weighted Matching....................................................................43
Distance Minimization.......................................................................44
Software.............................................................................................50
Summary............................................................................................50
Appendix: Job Application Data Set..................................................51
Part II DATA MINING METHODS AS TOOLS
4 Association Rules in Knowledge Discovery........................................... 53
Market-Basket Analysis.....................................................................55
Market Basket Analysis Benefits...............................................56
Demonstration on Small Set of Data ......................................... 57
Real Market Basket Data ................................................................... 59
The Counting Method Without Software ..................................62
Conclusions........................................................................................68
5 Fuzzy Sets in Data Mining...................................................................... 69
Fuzzy Sets and Decision Trees .......................................................... 71
Fuzzy Sets and Ordinal Classification ............................................... 75
Fuzzy Association Rules....................................................................79
Demonstration Model ................................................................80
Computational Results...............................................................84
Testing .......................................................................................84
Inferences...................................................................................85
Conclusions........................................................................................86
6 Rough Sets .............................................................................................. 87
A Brief Theory of Rough Sets ........................................................... 88
Information System....................................................................88
Decision Table ...........................................................................89
Some Exemplary Applications of Rough Sets................................... 91
Rough Sets Software Tools................................................................93
The Process of Conducting Rough Sets Analysis.............................. 93
1 Data Pre-Processing................................................................94
2 Data Partitioning .....................................................................95
3 Discretization ..........................................................................95
4 Reduct Generation ..................................................................97
5 Rule Generation and Rule Filtering ........................................ 99
6 Apply the Discretization Cuts to Test Dataset...................... 100
7 Score the Test Dataset on Generated Rule set (and
measuring the prediction accuracy) ...................................... 100
8 Deploying the Rules in a Production System ....................... 102
A Representative Example...............................................................103
Conclusion .......................................................................................109
7 Support Vector Machines ..................................................................... 111
Formal Explanation of SVM............................................................112
Primal Form.............................................................................114
Contents XI
Dual Form................................................................................114
Soft Margin..............................................................................114
Non-linear Classification .................................................................115
Regression................................................................................116
Implementation ........................................................................116
Kernel Trick.............................................................................117
Use of SVM – A Process-Based Approach .....................................118
Support Vector Machines versus Artificial Neural Networks .........121
Disadvantages of Support Vector Machines....................................122
8 Genetic Algorithm Support to Data Mining .........................................125
Demonstration of Genetic Algorithm ..............................................126
Application of Genetic Algorithms in Data Mining ........................131
Summary..........................................................................................132
Appendix: Loan Application Data Set.............................................133
9 Performance Evaluation for Predictive Modeling ................................137
Performance Metrics for Predictive Modeling ................................137
Estimation Methodology for Classification Models ........................140
Simple Split (Holdout).....................................................................140
The k-Fold Cross Validation............................................................141
Bootstrapping and Jackknifing ........................................................143
Area Under the ROC Curve.............................................................144
Summary..........................................................................................147
Part III APPLICATIONS
10 Applications of Methods ....................................................................151
Memory-Based Application.............................................................151
Association Rule Application ..........................................................153
Fuzzy Data Mining ..........................................................................155
Rough Set Models............................................................................155
Support Vector Machine Application ..............................................157
Genetic Algorithm Applications......................................................158
Japanese Credit Screening .......................................................158
Product Quality Testing Design...............................................159
Customer Targeting .................................................................159
Medical Analysis .....................................................................160
Predicting the Financial Success of Hollywood Movies ................. 162
Problem and Data Description.................................................163
Comparative Analysis of the Data Mining Methods ............... 165
Conclusions......................................................................................167
Bibliography ............................................................................................ 169
Index ........................................................................................................ 177

扫码或添加微信号：坛友素质互助

「经管之家」APP：经管人学习、答疑、交友，就上经管之家！
免流量费下载资料----在经管之家app可以下载论坛上的所有资源，并且不额外收取下载高峰期的论坛币。
涵盖所有经管领域的优秀内容----覆盖经济、管理、金融投资、计量统计、数据分析、国贸、财会等专业的学习宝库，各类资料应有尽有。
来自五湖四海的经管达人----已经有上千万的经管人来到这里，你可以找到任何学科方向、有共同话题的朋友。
经管之家（原人大经济论坛），跨越高校的围墙，带你走进经管知识的新世界。
扫描下方二维码下载并注册APP

本文关键词：

本文论坛网址：https://bbs.pinggu.org/thread-280466-1-1.html

上一篇 | 求助：关于宏观经济学的两个问题！

下一篇 | [下载]SAS.Publishing.Base.SAS.9.1.Dot. ...

Advanced软件培训精彩帖子推荐更多

您可能感兴趣的文章

本站推荐的文章

人气文章

本文标题：[下载]Springer08《高级数据挖掘技术》（Advanced Data Mining Techniques）

本文链接网址：https://bbs.pinggu.org/jg/ruanjianpeixun_advancedruanjianpeixun_280466_1.html

1.凡人大经济论坛-经管之家转载的文章,均出自其它媒体或其他官网介绍,目的在于传递更多的信息,并不代表本站赞同其观点和其真实性负责；
2.转载的文章仅代表原创作者观点,与本站无关。其原创性以及文中陈述文字和内容未经本站证实,本站对该文以及其中全部或者部分内容、文字的真实性、完整性、及时性，不作出任何保证或承若；
3.如本站转载稿涉及版权等问题,请作者及时联系本站,我们会及时处理。

[下载]Springer08《高级数据挖掘技术》（Advanced Data Mining Techniques）-经管之家官网！

Advanced软件培训