Data mining and machine learning : fundamental concepts and algorithms
著者
書誌事項
Data mining and machine learning : fundamental concepts and algorithms
Cambridge University Press, 2020
2nd ed
- : hardback
大学図書館所蔵 件 / 全15件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Previous edition: 2014
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
- 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.
「Nielsen BookData」 より
