数据挖掘基础教程(附光盘·影印版)

    数据挖掘就是发现数据模型，以助于解释当前行为或预测将来的可能结果。本书介绍了数据挖掘的基本过程，解释了如何将数据挖掘应用于解决实际问题，从而使你能将数据挖掘技术应用于自己的实际工作中去。本书讲述了数据挖掘和知识发现的各方面内容，并着重介绍了数据挖掘模型的建立与测试，以及数据挖掘结果的解释与验证等内容。为了使读者更好地理解数据挖掘过程，在本书配套光盘中提供了一个基于Microsoft Excel的数据挖掘工具，读者可以亲身体验数据挖掘模型的建立与测试。 　　本书可作为相关专业的本科生教材，对需要理解数据挖掘和智能系统的专业人员也是很好的参考书。

数据挖掘Clementine应用实务	数据挖掘算法与应用	金融数据挖掘	数据挖掘原理	空间数据挖掘理论与应用
数据挖掘导论——图灵计算机科学丛书	数据挖掘：概念与技术（英文版·第2版）	数据挖掘原理与算法（第二版）	数据挖掘原理与算法	生物医学数据挖掘

    

Part I Data Mining Fundamentals
chapter 1 Data Mining:A First View
1.1 Data Mining:A Definition
1.2 What Can Computers Learn?
Three concept Views
Supervised Learing
Supervised Learing:A Decision for Tree Example
Unsupervised Clustering
1.3 Is Data Mining Appropriate for My Problem?
Data Mining or Data Query?
Data Mining vs.Data Query:An Example
1.4 Expert Systems or Data Mining?
1.5 A Simple Data Mining Process Model
Assembling the Data
The Data Warehouse
Relational Databases and Flat Files
Mining the Data
Interpreting the Results
Result application
1.6 Why Not Simple Search?
1.7 Data Mining Applications
Example Applications
Customer Intrinsic Value
1.8 chapter Summary
1.9 Key Terms
1.10 Exercises
Chapter 2 Data Mining:A closer Look
2.1 Data Mining Strategies
classification
Estimation
Prediction
Unsupervised clustering
Market Basket Ananlysis
2.2 Supervised Data Mining Database
the Credit Card Promotion Database
Production Rules
Neural Networks
Statistical Regression
2.3 Association Rules
2.4 Clustering techniques
2.5 Evaluating Performance
evaluating supervised Learner Models
Two Class Error Analysis
Evaluating Numeric Output
Unsupervised Moedl Evaluation
2.6 chapter Summary
2.7 Key Terms
2.8 Exercises
Chapter 3 Basic Data Mining Techniques
Chapter 4 An Excel-Based Data Mining Tool
Part 2 Advanced Data Mining Techniques
Chapter 8 Nerual Networks
Chapter 9 Building Nerual Networks with IDA
Chapter 10 Staticstical Techniques
Chapter 11 Specialized Techniques
Part 4:Intelligent Systems
Chapter 12 Rule-Based Systems
Chapter 13 Managing Uncertainty in Rule-Based System
Chapter 14 Intelligent Agents
Appendixes
Appendix A The iDASoftware
Appendix B Datasets for Data Mining
Appendix C Decision Tree Atrribute Selection
Appendix D Statistics for Performance Evaluation
Appendix E Excel Pivot Tables:Office 97
Bibliography
Index

Data mining is the process of finding useful patterns in data. The objective of data mining is to use discovered patterns to help explain current behavior or to predict future outcomes. Several aspects of the data mining process can be studied. These include: · Data gathering and storage · Data selection and preparation · Model building and testing · Interpreting and validating results · Model application A single book cannot concentrate on all areas of the data mining process. Although we furnish some detail about all aspect..