數(shù)據(jù)挖掘：實(shí)用機(jī)器學(xué)習(xí)工具與技術(shù)(第4版)是數(shù)據(jù)挖掘和機(jī)器學(xué)習(xí)領(lǐng)域的經(jīng)典暢銷教材，被國內(nèi)外眾多名校選用。第4版全面反映了該領(lǐng)域的新技術(shù)變革，包括關(guān)于概率方法和深度學(xué)習(xí)的重要新章節(jié)。此外，備受歡迎的機(jī)器學(xué)習(xí)軟件Weka再度升級(jí)，讀者可以在友好的交互界面中執(zhí)行數(shù)據(jù)挖掘任務(wù)。

書中的基礎(chǔ)知識(shí)清晰詳細(xì)，實(shí)踐工具和技術(shù)指導(dǎo)具體實(shí)用，不僅適合作為高等院校相關(guān)專業(yè)的本科生或研究生教材，也可供廣大技術(shù)人員參考。

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