Data Mining

Höfundar: James Foulds; Ian H. Witten; Eibe Frank; Mark A. Hall; Christopher J. Pal (Útgáfa: 5)
Data Mining

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Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.

Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on topics such as generative AI (GANs, VAEs, diffusion models), large language models (transformers, BERT and GPT models), and adversarial examples, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics.

Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new author James R. Foulds, include today’s techniques coupled with the methods at the leading edge of contemporary researchProvides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projectsPresents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methodsFeatures in-depth information on deep learning and probabilistic modelsCovers performance improvement techniques, including input preprocessing and combining output from different methodsProvides an appendix introducing the WEKA machine learning workbench and links to algorithm implementations in the softwareIncludes all-new exercises for each chapter.

Nánar um bókina

Útgefandi
Elsevier S & T
ISBN
9780443158896
Print ISBN
9780443158889
Format
ePub
Útgáfa
5
Höfundar
James Foulds; Ian H. Witten; Eibe Frank; Mark A. Hall; Christopher J. Pal
Tungumál
English
Útgefið
2025-02-04
Prent takmörkun á líftíma
10

Kaflar

  • Title of Book
  • Cover image
  • Title page
  • Copyright
  • Dedication
  • Table of Contents
  • List of Figures
  • List of Tables
  • Preface
  • Updated and Revised Content
  • Acknowledgments
  • Part I: Introduction To Data Mining
  • Chapter 1. What’s it all about?
  • Abstract
  • Chapter Outline
  • 1.1 Data Mining and Machine Learning
  • 1.2 Simple Examples: The Weather Problem and Others
  • 1.3 Fielded Applications
  • 1.4 The Data Mining Process
  • 1.5 Machine Learning, Statistics, and Artificial Intelligence
  • 1.6 Generalization as Search
  • 1.7 Data Mining and Ethics
  • 1.8 Further Reading and Bibliographic Notes
  • 1.9 Exercises
  • Chapter 2. Input: concepts, instances, attributes
  • Abstract
  • Chapter Outline
  • 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 and Bibliographic Notes
  • 2.6 Exercises
  • chapter 3. Output: knowledge representation
  • Abstract
  • Chapter Outline
  • 3.1 Tables
  • 3.2 Linear Models
  • 3.3 Trees
  • 3.4 Rules
  • 3.5 Instance-Based Representation
  • 3.6 Clusters
  • 3.7 Further Reading and Bibliographic Notes
  • 3.8 Exercises
  • Chapter 4. Algorithms: the basic methods
  • Abstract
  • Chapter Outline
  • 4.1 Inferring Rudimentary Rules
  • 4.2 Simple Probabilistic 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 Multiinstance Learning
  • 4.10 Further Reading and Bibliographic Notes
  • 4.11 Weka Implementations
  • 4.12 Exercises
  • Chapter 5. Credibility: evaluating what’s been learned
  • Abstract
  • Chapter Outline
  • 5.1 Training and Testing
  • 5.2 Predicting Performance
  • 5.3 Cross-Validation
  • 5.4 Other Estimates
  • 5.5 Hyperparameter Selection
  • 5.6 Comparing Data Mining Schemes
  • 5.7 Predicting Probabilities
  • 5.8 Counting the Cost
  • 5.9 Evaluating Numeric Prediction
  • 5.10 The MDL Principle
  • 5.11 Applying the MDL Principle to Clustering
  • 5.12 Using a Validation Set for Model Selection
  • 5.13 Further Reading and Bibliographic Notes
  • 5.14 Exercises
  • Chapter 6. Preparation: data preprocessing and exploratory data analysis
  • Abstract
  • Chapter Outline
  • 6.1 Attribute Selection
  • 6.2 Discretizing Numeric Attributes
  • 6.3 Projections
  • 6.4 Sampling
  • 6.5 Cleansing
  • 6.6 Transforming Multiple Classes to Binary Ones
  • 6.7 Calibrating Class Probabilities
  • 6.8 Exploratory Data Analysis
  • 6.9 Further Reading and Bibliographic Notes
  • 6.10 Weka Implementations
  • 6.11 Exercises
  • Chapter 7. Ethics: what are the impacts of what’s been learned?
  • Abstract
  • Chapter Outline
  • 7.1 Privacy
  • 7.2 Fairness and Bias in Machine Learning
  • 7.3 AI Safety
  • 7.4 Further Reading and Bibliographic Notes
  • 7.5 Exercises
  • Part II. More advanced machine learning schemes
  • Chapter 8. Ensemble learning
  • Abstract
  • Chapter Outline
  • 8.1 Combining Multiple Models
  • 8.2 Bagging
  • 8.3 Randomization
  • 8.4 Boosting
  • 8.5 Additive Regression
  • 8.6 Interpretable Ensembles
  • 8.7 Stacking
  • 8.8 Further Reading and Bibliographic Notes
  • 8.9 WEKA Implementations
  • 8.10 Exercises
  • chapter 9. Extending instance-based and linear models
  • Abstract
  • Chapter Outline
  • 9.1 Instance-Based Learning
  • 9.2 Extending Linear Models
  • 9.3 Numeric Prediction WIth Local Linear Models
  • 9.4Weka Implementations
  • 9.5 Exercises
  • Chapter 10. Deep learning: fundamentals
  • Abstract
  • Chapter Outline
  • 10.1 Deep Feedforward Networks
  • 10.2 Training and Evaluating Deep Networks
  • 10.3 Convolutional Neural Networks
  • 10.4 Autoencoders
  • 10.5 Recurrent Neural Networks
  • 10.6 Further Reading and Bibliographic Notes
  • 10.7 Deep Learning Software and Network Implementations
  • 10.8 WEKA Implementations
  • 10.9 Exercises
  • Chapter 11. Advanced deep learning methods
  • Abstract
  • Chapter Outline
  • 11.1 Generative AI via Deep Learning
  • 11.2 Introduction to Natural Language Processing and Large Language Models
  • 11.3 Transformer Architecture
  • 11.4 Transformer-Based Language Models
  • 11.5 Adversarial Examples
  • 11.6 Knowledge Distillation
  • 11.7 Deep Reinforcement Learning
  • 11.8 Further Reading and Bibliographic Notes
  • 11.9 Exercises
  • Chapter 12. Beyond supervised and unsupervised learning
  • Abstract
  • Chapter Outline
  • 12.1 Semisupervised Learning
  • 12.2 Multi-instance Learning
  • 12.3 Further Reading and Bibliographic Notes
  • 12.4 WEKA Implementations
  • 12.5 Exercises
  • Chapter 13. Probabilistic methods: fundamentals
  • Abstract
  • Chapter Outline
  • 13.1 Foundations
  • 13.2 Bayesian Networks
  • 13.3 Clustering and Probability Density Estimation
  • 13.4 Further Reading and Bibliographic Notes
  • 13.5 Exercises
  • Chapter 14. Advanced probabilistic methods
  • Abstract
  • Chapter Outline
  • 14.1 Hidden Variable Models
  • 14.2 Bayesian Estimation and Prediction
  • 14.3 Graphical Models and Factor Graphs
  • 14.4 Conditional Probability Models
  • 14.5 Sequential and Temporal Models
  • 14.6 Further Reading and Bibliographic Notes
  • 14.7 WEKA Implementations
  • 14.8 Exercises
  • Chapter 15. Moving on: applications and their consequences
  • Abstract
  • Chapter Outline
  • 15.1 Applying Machine Learning
  • 15.2 Learning from Massive Datasets
  • 15.3 Data Stream Learning
  • 15.4 Incorporating Domain Knowledge
  • 15.5 Text Mining
  • 15.6 Web Mining
  • 15.7 Images and Speech
  • 15.8 Adversarial Situations
  • 15.9 Ubiquitous Data Mining
  • 15.10 Machine Learning Technologies and Applications of Concern
  • 15.11 AI and Society
  • 15.12 Further Reading and Bibliographic Notes
  • 15.13 WEKA Implementations
  • 15.14 Exercises
  • Appendix A. : Theoretical foundations
  • A.1 Matrix Algebra
  • A.2 Fundamental Elements of Probabilistic Methods
  • Appendix B. : The WEKA workbench
  • B.1 What’s in WEKA?
  • B.2 The Package Management System
  • B.3 The Explorer
  • B.4 The Knowledge Flow Interface
  • B.5 The Experimenter
  • Appendix C. : Implementation details of trees and rules
  • C.1 Decision Trees
  • C.2 Classification Rules
  • C.3 Association Rules
  • C.4 WEKA Implementations
  • Appendix D. : Technical details of deep learning
  • D.1 Backpropagation Revisited
  • D.2 Gradient Calculations for CNNs
  • D.3 Stochastic Deep Networks
  • D.4 Further Reading and Bibliographic Notes
  • References
  • Index