Data Mining and Machine Learning
Fundamental Concepts and Algorithms

New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.

Mohammed J. Zaki (Author), Wagner Meira, Jr (Author)

9781108473989, Cambridge University Press

Hardback, published 30 January 2020

776 pages, 297 b/w illus.
25.7 x 18.5 x 4.5 cm, 1.6 kg

'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

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.

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.

Subject Areas: Pattern recognition [UYQP], Machine learning [UYQM], Data mining [UNF], Databases [UN], Knowledge management [KJMV3]