书籍详情
数据挖掘：英文版 实用机器学习技术
作者：（新西兰）Lan H.Witten，（新西兰）Eibe Frank著
出版社：机械工业出版社
出版时间：2005-09-01
ISBN：9787111172482
定价：¥58.00
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内容简介
“本书将这门新的学科用一种非常容易理解的方式呈现给读者：它既是一本用于培训新一代实践者和研究工作者的教科书，同时对于我这样需要不断充电的专业读者也极具启示作用。Witten和Frank热切追求的是简单而流畅的解决方案，他们时刻不忘将所有的概念都建立在具体实例的基础之上，促使读者首先考虑简单的技术，如果这些简单技术不足以解决问题，再进一步考虑更为高级和成熟的技术。 假如你想分析和理解数据，本书以及相关的Weka工具包将非常有用。” ――摘自微软研究院图灵奖得主Jim Gray所做的前言本书对1999年的初版做了重大的改动。虽说核心概念没有变化，但本书进行了更新使其能反映过去5年里的变化，参考文献几乎翻了一番。新版的重要部分包括：30个新的技术章节；一个加强了的具有交互式界面的Weka机器学习工作平台；有关神经网络的完整信息，一个有关贝叶斯网络的新节；等等。 本书提供了机器学习概念的完整基础，此外还针对实际工作中应用相关工具和技术提出了一些建议，在本书中你将发现： ●成功数据挖掘技术的核心算法，包括历经考验的真实技术及前沿的方法。 ●转换输入或输出以改善性能的方法。 ●可下载的Weka软件？？一个用于数据挖掘任务的机器学习算法的集合，包括用于数据预处理、分类、回归、聚类、关联规则以及在新的交互式界面上可视化的工具。
作者简介
LanH.Witten新西兰怀卡托大学计算机科学系教授，ACM和新西兰皇家学会成员。他曾荣获2004年国际信息处理研究联合会颁发的Namur奖项，这是一个两年一度的荣誉奖项，用于奖励那些在信息和通信技术的社会应用方面做出杰现贡献及具有国际影响的人。他的著作包括《ManagingGigabytes》（1999）、《HowtoBuildaDigitalLibrary》（2003），以及众多的期刊文章和会议论文。EibeFrank，新西兰怀卡托大学计算机科学系高级讲师。他在机器学习领域发表了大量的论文，是《MachineLearingJournal》和《JournalofArtificialIntelligenceResearch》的编委之一。同时他还是许多数据挖掘和机器学习学术会议设计委员会的成员。作为Weka机器学习软件的核心开发成员之一，他维护并不断完善着这个软件。相关图书软件过程改进（英文版）80X86汇编语言与计算机体系结构计算机科学概论（英文版·第2版）分布式系统概念设计调和分析导论（英文第三版）人工智能：智能系统指南（英文版）第二版电力系统分析（英文版·第2版）面向计算机科学的数理逻辑：系统建模与推理（英文版·第2版）机器视觉教程（英文版）（含盘）支持向量机导论（英文版）Java教程（英文版·第2版）软件需求管理：用例方法（英文版·第2版）数字通信导论离散事件系统仿真（英文版·第4版）复杂SoC设计（英文版）基于FPGA的系统设计（英文版）实用软件工程（英文版）UNIX教程（英文版·第2版）软件测试（英文版第2版）设计模式精解（英文版第2版）Linux内核编程必读-经典原版书库实分析和概率论-经典原版书库（英文版.第2版）计算机体系结构：量化研究方法：第3版数学规划导论英文版抽样理论与方法（英文版）Java2专家导引（英文版·第3版）复分析基础及工程应用（英文版.第3版）电子设计自动化基础（英文版）Java程序设计导论（英文版·第5版）UML参考手册（第2版）UML参考手册（英文版·第2版）计算理论导引计算机取证（英文版）EffectiveC#（英文版）基于用例的面向方面软件开发（英文版）
目录
Foreword
Preface
Part I Machine learning tools and techniques
1. What?s it all about?
1.1 Data mining and machine learning
1.2 Simple examples: the weather problem and others
1.3 Fielded applications
1.4 Machine learning and statistics
1.5 Generalization as search
1.6 Data mining and ethics
1.7 Further reading
2. Input: Concepts, instances, attributes
2.1 What?s a concept?
2.2 What?s in an example?
2.3 What?s in an attribute?
2.4 Preparing the input
2.5 Further reading
3. Output: Knowledge representation
3.1 Decision tables
3.2 Decision trees
3.3 Classification rules
3.4 Association rules
3.5 Rules with exceptions
3.6 Rules involving relations
3.7 Trees for numeric prediction
3.8 Instance-based representation
3.9 Clusters
3.10 Further reading
4. Algorithms: The basic methods
4.1 Inferring rudimentary rules
4.2 Statistical modeling
4.3 Divide-and-conquer: constructing decision trees
4.4 Covering algorithms: constructing rules
4.5 Mining association rules
4.6 Linear models
4.7 Instance-based learning
4.8 Clustering
4.9 Further reading
5. Credibility: Evaluating what?s been learned
5.1 Training and testing
5.2 Predicting performance
5.3 Cross-validation
5.4 Other estimates
5.5 Comparing data mining schemes
5.6 Predicting probabilities
5.7 Counting the cost
5.8 Evaluating numeric prediction
5.9 The minimum description length principle
5.10 Applying MDL to clustering
5.11 Further reading
6. Implementations: Real machine learning schemes
6.1 Decision trees
6.2 Classification rules
6.3 Extending linear models
6.4 Instance-based learning
6.5 Numeric prediction
6.6 Clustering
6.7 Bayesian networks
7. Transformations: Engineering the input and output
7.1 Attribute selection
7.2 Discretizing numeric attributes
7.3 Some useful transformations
7.4 Automatic data cleansing
7.5 Combining multiple models
7.6 Using unlabeled data
7.7 Further reading
8. Moving on: Extensions and applications
8.1 Learning from massive datasets
8.2 Incorporating domain knowledge
8.3 Text and Web mining
8.4 Adversarial situations
8.5 Ubiquitous data mining
8.6 Further reading
Part II: The Weka machine learning workbench
9. Introduction to Weka
9.1 What?s in Weka?
9.2 How do you use it?
9.3 What else can you do?
9.4 How do you get it?
10. The Explorer
10.1 Getting started
10.2 Exploring the Explorer
10.3 Filtering algorithms
10.4 Learning algorithms
10.5 Meta-learning algorithms
10.6 Clustering algorithms
10.7 Association-rule learners
10.8 Attribute selection
11. The Knowledge Flow interface
11.1 Getting started
11.2 Knowledge Flow components
11.3 Configuring and connecting the components
11.4 Incremental learning
12. The Experimenter
12.1 Getting started
12.2 Simple setup
12.3 Advanced setup
12.4 The Analyze panel
12.5 Distributing processing over several machines
13. The command-line interface
13.1 Getting started
13.2 The structure of Weka
13.3 Command-line options
14. Embedded machine learning
……
15. Writing new learning schemes
References
Index
Preface
Part I Machine learning tools and techniques
1. What?s it all about?
1.1 Data mining and machine learning
1.2 Simple examples: the weather problem and others
1.3 Fielded applications
1.4 Machine learning and statistics
1.5 Generalization as search
1.6 Data mining and ethics
1.7 Further reading
2. Input: Concepts, instances, attributes
2.1 What?s a concept?
2.2 What?s in an example?
2.3 What?s in an attribute?
2.4 Preparing the input
2.5 Further reading
3. Output: Knowledge representation
3.1 Decision tables
3.2 Decision trees
3.3 Classification rules
3.4 Association rules
3.5 Rules with exceptions
3.6 Rules involving relations
3.7 Trees for numeric prediction
3.8 Instance-based representation
3.9 Clusters
3.10 Further reading
4. Algorithms: The basic methods
4.1 Inferring rudimentary rules
4.2 Statistical modeling
4.3 Divide-and-conquer: constructing decision trees
4.4 Covering algorithms: constructing rules
4.5 Mining association rules
4.6 Linear models
4.7 Instance-based learning
4.8 Clustering
4.9 Further reading
5. Credibility: Evaluating what?s been learned
5.1 Training and testing
5.2 Predicting performance
5.3 Cross-validation
5.4 Other estimates
5.5 Comparing data mining schemes
5.6 Predicting probabilities
5.7 Counting the cost
5.8 Evaluating numeric prediction
5.9 The minimum description length principle
5.10 Applying MDL to clustering
5.11 Further reading
6. Implementations: Real machine learning schemes
6.1 Decision trees
6.2 Classification rules
6.3 Extending linear models
6.4 Instance-based learning
6.5 Numeric prediction
6.6 Clustering
6.7 Bayesian networks
7. Transformations: Engineering the input and output
7.1 Attribute selection
7.2 Discretizing numeric attributes
7.3 Some useful transformations
7.4 Automatic data cleansing
7.5 Combining multiple models
7.6 Using unlabeled data
7.7 Further reading
8. Moving on: Extensions and applications
8.1 Learning from massive datasets
8.2 Incorporating domain knowledge
8.3 Text and Web mining
8.4 Adversarial situations
8.5 Ubiquitous data mining
8.6 Further reading
Part II: The Weka machine learning workbench
9. Introduction to Weka
9.1 What?s in Weka?
9.2 How do you use it?
9.3 What else can you do?
9.4 How do you get it?
10. The Explorer
10.1 Getting started
10.2 Exploring the Explorer
10.3 Filtering algorithms
10.4 Learning algorithms
10.5 Meta-learning algorithms
10.6 Clustering algorithms
10.7 Association-rule learners
10.8 Attribute selection
11. The Knowledge Flow interface
11.1 Getting started
11.2 Knowledge Flow components
11.3 Configuring and connecting the components
11.4 Incremental learning
12. The Experimenter
12.1 Getting started
12.2 Simple setup
12.3 Advanced setup
12.4 The Analyze panel
12.5 Distributing processing over several machines
13. The command-line interface
13.1 Getting started
13.2 The structure of Weka
13.3 Command-line options
14. Embedded machine learning
……
15. Writing new learning schemes
References
Index
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