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.
read now: Understanding Health Insurance: A Guide to Billing and Reimbursement 17th Edition
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.
read this: Medical Terminology for Dummies 3rd Edition by Beverley Henderson Review
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
Not much found about the author’s profile. Only his name was found. But you can look for it further on the internet page.
If you find information about the author of this book, please write it in the comments column.
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
Not much found about the author’s profile. Only his name was found. But you can look for it further on the internet page.
If you find information about the author of this book, please write it in the comments column.
You can find a lot of information from this book if you have it. The way to get it is to buy it at the nearest bookstore. Or you can borrow a book from a friend.
But if you still want to have the book for free, it can be done by downloading it for free on the internet page. Usually there are PDF files that are shared by others.
Other types are EPUB and Kindle files made by Amazon which can be purchased online.
