数据挖掘基础教程

作者: 贾茨著 , 罗伊尔

出版社: 清华大学出版社

出版时间: 2003-12

版次: 1

ISBN: 9787302076674

定价: 43.00

装帧: 平装

开本: 其他

纸张: 胶版纸

页数: 350页

分类: 教材教辅考试>教材>大学教材>工程技术

5人买过

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。 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
内容简介:
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。
目录:
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

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数据挖掘基础教程

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