数据挖掘
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作者(新西兰)伊恩 H.威腾(Ian H.Witten) 等 著
出版社机械工业出版社
ISBN9787111565277
出版时间2017-04
装帧平装
开本16开
定价129元
货号1201503366
上书时间2024-08-09
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作者简介
伊恩 H.威腾，新西兰怀卡托大学计算机科学系教授，ACM会士，新西兰皇家学会会士，曾荣获2004年靠前信息处理研究协会(1FIP)颁发的Namur奖。
目录
Preface
PART I INTRODUCTION TO DATA MINING
CHAPTER 1Whats it all about?
1.1 Data Mining and Machine Learning
Describing Structural Patterns
Machine Learning
Data Mining
1.2 Simple Examples: The Weather Problem and Others
The Weather Problem
Contact Lenses: An Idealized Problem
Irises: A Classic Numeric Dataset
CPU Performance: Introducing Numeric Prediction
Labor Negotiations: A More Realistic Example
Soybean Classification: A Classic Machine Learning
Success
1.3 Fielded Applications
Web Mining
Decisions Involving Judgment
Screening Images
Load Forecasting
Diagnosis
Marketing and Sales
Other Applications
1.4 The Data Mining Process
1.5 Machine Learning and Statistics
1.6 Generalization as Search
Enumerating the Concept Space
Bias
1.7 Data Mining and Ethics
Reidentification
Using Personal Information
Wider Issues
1.8 Further Reading and Bibliographic Notes
CHAPTER 2 Input: concepts, instances, attributes
2.1 Whats a Concept?
2.2 Whats in an Example?
Relations
Other Example Types
2.3 Whats in an Attribute?
2.4 Preparing the Input
Gathering the Data Together
ARFF Format
Sparse Data
Attribute Types
Missing Values
Inaccurate Values
Unbalanced Data
Getting to Know Your Data
2.5 Further Reading and Bibliographic Notes
CHAPTER 3 Output: knowledge representation
3.1 Tables
3.2 Linear Models
3.3 Trees
3.4 Rules
Classification Rules
Association Rules
Rules With Exceptions
More Expressive Rules
3.5 Instance-Based Representation
3.6 Clusters
3.7 Further Reading and Bibliographic Notes
CHAPTER 4 Algorithms: the basic methods
4.1 Inferring Rudimentary Rules
Missing Values and Numeric Attributes
4.2 Simple Probabilistic Modeling
Missing Values and Numeric Attributes
Naive Bayes for Document Classification
Remarks
4.3 Divide-and-Conquer: Constructing Decision Trees
Calculating Information
Highly Branching Attributes
……
CHAPTER 5 Credibility: evaluating whats been learned
PART II MORE ADVANCED MACHINE LEARNING SCHEMES
CHAPTER 6 Trees and rules
CHAPTER 7 Extending instance-based and linear models
CHAPTER 8 Data transformations
CHAPTER 9 Probalicistic methods
CHAPTER 10 Deep learning
CHAPTER 11 Beyond supervised and unsupervised learning
CHAPTER 12 Ensemble learning
CHAPTER 13 Moving on: applications and beyond
Appendix A: Theoretical foundations
Appendix B: The WEKA workbench
References
Index
内容摘要
本书是数据挖掘和机器学习领域的经典畅销教材，被靠前外众多名校选用。第4版全面反映了该领域的新技术变革，包括关于概率方法和深度学习的重要新章节。此外，备受欢迎的机器学习软件Weka再度升级，读者可以在友好的交互界面中执行数据挖掘任务。书中的基础知识清晰详细，实践工具和技术指导具体实用，不仅适合作为高等院校相关专业的本科生或研究生教材，也可供广大技术人员参考。
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