《數據挖掘：實用機器學習工具與技術》是2017年機械工業出版社出版的圖書，作者是伊恩 H. 威騰。
基本介紹
- 中文名：數據挖掘：實用機器學習工具與技術
- 作者：伊恩 H. 威騰
- 出版時間：2017年5月
- 出版社：機械工業出版社
- ISBN：9787111565277
- 類別：數據倉庫與數據挖掘
- 開本：16 開
- 裝幀：平裝-膠訂
內容簡介,圖書目錄,
內容簡介
本書是數據挖掘和機器學習領域的經典暢銷教材，被國內外眾多名校選用。第4版全面反映了該領域的新技術變革，包括關於機率方法和深度學習的重要新章節。此外，備受歡迎的機器學習軟體Weka再度升級，讀者可以在友好的互動界面中執行數據挖掘任務。書中的基礎知識清晰詳細，實踐工具和技術指導具體實用，不僅適合作為高等院校相關專業的本科生或研究生教材，也可供廣大技術人員參考。
圖書目錄
Preface
PART I INTRODUCTION TO DATA MINING
CHAPTER 1 What's 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
1.4The 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
CHAPTER 3 Output: knowledge representation
CHAPTER 4 Algorithms: the basic methods
CHAPTER 5 Credibility: evaluating what's 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 Probabilistic methods
Chapter 10 Deep learning
CHAPTER 11 Beyond supervised and unsupervised learning
CHAPTER 12 Ensemble learning
CHAPTER 13 Moving on : applications and beyond
List of Figures
List of Tables
