Data Mining and Machine Learning
Data Mining and Machine Learning
Wagner Meira, Jr, Universidade Federal de Minas Gerais, Brazil
Experience the eBook and the associated online resources on our new Higher Education website.
Go to site
For other formats please stay on this page.
£62.99
GBPThe 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
Reviews & endorsements
'This book by Mohammed Zaki and Wagner Meira, Jr is a great option for teaching a course in data mining or data science. It covers both fundamental and advanced data mining topics, explains the mathematical foundations and the algorithms of data science, includes exercises for each chapter, and provides data, slides and other supplementary material on the companion website.' Gregory Piatetsky-Shapiro, Founder of the Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining (ACM SIGKDD)
'World-class experts, providing an encyclopedic coverage of all datamining topics, from basic statistics to fundamental methods (clustering, classification, frequent itemsets), to advanced methods (SVD, SVM, kernels, spectral graph theory, deep learning). For each concept, the book thoughtfully balances the intuition, the arithmetic examples, as well the rigorous math details. It can serve both as a textbook, as well as a reference book.' Christos Faloutsos, Carnegie Mellon University, Pennsylvania, and winner of the ACM SIGKDD Innovation Award
Product details
January 2020Hardback
9781108473989
776 pages
257 × 185 × 45 mm
1.6kg
297 b/w illus.
Available
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.
