Data Mining: Concepts and Techniques 4th Edition by Jiawei Han Review

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Data Mining: Concepts and Techniques 4th Edition, pdf, ebook, and free download by Jiawei Han, Micheline Kamber
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Data Mining: Concepts and Techniques 4th Edition, pdf, ebook, and free download by Jiawei Han, Micheline Kamber provides the ideas and methods in processing collected information or information, which will be used in various applications. Particularly, it explains information mining and the devices used in finding knowledge from the gathered information.

This message (book) is referred to as the understanding exploration from the information. It concentrates on the expediency, effectiveness, efficiency, and scalability of methods of big information collections. After explaining information mining, this version discusses the techniques of understanding, preprocessing, refining, and warehousing information.

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It after that provides information about information warehouses, online logical processing (OLAP), and information dice technology. After that, the techniques associated with mining regular patterns, organizations, and correlations for large information sets are explained.

Guide information on the techniques for information category and presents the ideas and techniques for information clustering. The remaining chapters discuss outlier discovery and the trends, applications, and research frontiers in information mining.

This book is intended for Computer system Scientific research trainees, application developers, business experts, and scientists that look for information on information mining.

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Some important information in the book is based on the Amazon page, as follows:

Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects.

Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields.

Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data.

About of Author

Jiawei Han

Micheline Kamber

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Book Details

Publisher ‏ : ‎ Morgan Kaufmann; fourth edition (October 15, 2022)
Language ‏ : ‎ English
Paperback ‏ : ‎ 752 pages
ISBN-10 ‏ : ‎ 0128117605
ISBN-13 ‏ : ‎ 978-0128117606
Item Weight ‏ : ‎ 1.11 pounds

Table of Contents

Part I: Introduction to data mining

Chapter 1. What’s it all about?

Abstract

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 and Statistics

1.6 Generalization as Search

1.7 Data Mining and Ethics

1.8 Further Reading and Bibliographic Notes

Chapter 2. Input: Concepts, instances, attributes

Abstract

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

Chapter 3. Output: Knowledge representation

Abstract

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

Chapter 4. Algorithms: The basic methods

Abstracts

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 Multi-instance Learning

4.10 Further Reading and Bibliographic Notes

4.11 Weka Implementations

Chapter 5. Credibility: Evaluating what’s been learned

Abstract

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

Part II: More advanced machine learning schemes

Chapter 6. Trees and rules

Abstract

6.1 Decision Trees

6.2 Classification Rules

6.3 Association Rules

6.4 Weka Implementations

Chapter 7. Extending instance-based and linear models

Abstract

7.1 Instance-Based Learning

7.2 Extending Linear Models

7.3 Numeric Prediction With Local Linear Models

7.4 Weka Implementations

Chapter 8. Data transformations

Abstracts

8.1 Attribute Selection

8.2 Discretizing Numeric Attributes

8.3 Projections

8.4 Sampling

8.5 Cleansing

8.6 Transforming Multiple Classes to Binary Ones

8.7 Calibrating Class Probabilities

8.8 Further Reading and Bibliographic Notes

8.9 Weka Implementations

Chapter 9. Probabilistic methods

Abstract

9.1 Foundations

9.2 Bayesian Networks

9.3 Clustering and Probability Density Estimation

9.4 Hidden Variable Models

9.5 Bayesian Estimation and Prediction

9.6 Graphical Models and Factor Graphs

9.7 Conditional Probability Models

9.8 Sequential and Temporal Models

9.9 Further Reading and Bibliographic Notes

9.10 Weka Implementations

Chapter 10. Deep learning

Abstract

10.1 Deep Feedforward Networks

10.2 Training and Evaluating Deep Networks

10.3 Convolutional Neural Networks

10.4 Autoencoders

10.5 Stochastic Deep Networks

10.6 Recurrent Neural Networks

10.7 Further Reading and Bibliographic Notes

10.8 Deep Learning Software and Network Implementations

10.9 WEKA Implementations

Chapter 11. Beyond supervised and unsupervised learning

Abstract

11.1 Semisupervised Learning

11.2 Multi-instance Learning

11.3 Further Reading and Bibliographic Notes

11.4 WEKA Implementations

Chapter 12. Ensemble learning

Abstract

12.1 Combining Multiple Models

12.2 Bagging

12.3 Randomization

12.4 Boosting

12.5 Additive Regression

12.6 Interpretable Ensembles

12.7 Stacking

12.8 Further Reading and Bibliographic Notes

12.9 WEKA Implementations

Chapter 13. Moving on: applications and beyond

Abstract

13.1 Applying Machine Learning

13.2 Learning From Massive Datasets

13.3 Data Stream Learning

13.4 Incorporating Domain Knowledge

13.5 Text Mining

13.6 Web Mining

13.7 Images and Speech

13.8 Adversarial Situations

13.9 Ubiquitous Data Mining

13.10 Further Reading and Bibliographic Notes

13.11 WEKA Implementations

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

Book Format

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