Data Mining and Machine Learning: Fundamental Concepts and Algorithms 2/e
售價
$
1,900
- 一般書籍
- ISBN：9781108473989
- 作者：Mohammed J. Zaki, Wagner Meira, Jr.
- 版次：2
- 年份：2020
- 出版商：Cambridge University
- 頁數/規格：776頁/精裝單色
- 參考網頁：Data Mining and Machine Learning: Fundamental Concepts and Algorithms 2/e
書籍介紹
目錄
作者介紹
Description
The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.
The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.
- Covers both core methods and cutting-edge research, including deep learning
- Offers an algorithmic approach with open-source implementations
- Short, self-contained chapters with class-tested examples and exercises allow flexibility in course design and ready reference
Table of Contents
1. Data mining and analysis
Part I. Data Analysis Foundations:
2. Numeric attributes
3. Categorical attributes
4. Graph data
5. Kernel methods
6. High-dimensional data
7. Dimensionality reduction
Part II. Frequent Pattern Mining:
8. Itemset mining
9. Summarizing itemsets
10. Sequence mining
11. Graph pattern mining
12. Pattern and rule assessment
Part III. Clustering:
13. Representative-based clustering
14. Hierarchical clustering
15. Density-based clustering
16. Spectral and graph clustering
17. Clustering validation
Part IV. Classification:
18. Probabilistic classification
19. Decision tree classifier
20. Linear discriminant analysis
21. Support vector machines
22. Classification assessment
Part V. Regression:
23. Linear regression
24. Logistic regression
25. Neural networks
26. Deep learning
27. Regression evaluation.
1. Data mining and analysis
Part I. Data Analysis Foundations:
2. Numeric attributes
3. Categorical attributes
4. Graph data
5. Kernel methods
6. High-dimensional data
7. Dimensionality reduction
Part II. Frequent Pattern Mining:
8. Itemset mining
9. Summarizing itemsets
10. Sequence mining
11. Graph pattern mining
12. Pattern and rule assessment
Part III. Clustering:
13. Representative-based clustering
14. Hierarchical clustering
15. Density-based clustering
16. Spectral and graph clustering
17. Clustering validation
Part IV. Classification:
18. Probabilistic classification
19. Decision tree classifier
20. Linear discriminant analysis
21. Support vector machines
22. Classification assessment
Part V. Regression:
23. Linear regression
24. Logistic regression
25. Neural networks
26. Deep learning
27. Regression evaluation.
Mohammed J. Zaki, Rensselaer Polytechnic Institute, New York
Wagner Meira, Jr., Universidade Federal de Minas Gerais, Brazil
Wagner Meira, Jr., Universidade Federal de Minas Gerais, Brazil
