Data Mining and Machine Learning: Fundamental Concepts and Algorithms

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Mohammed J. Zaki, Wagner Meira JrCambridge University PressEdition: 2, 1/30/2020EAN 9781108473989, ISBN10: 1108473989Hardcover, 776 pages, 25.7 x 18.5 x 4.6 cmLanguage: EnglishThe 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 analysisPart I. Data Analysis Foundations2. Numeric attributes3. Categorical attributes4. Graph data5. Kernel methods6. High-dimensional data7. Dimensionality reductionPart II. Frequent Pattern Mining8. Itemset mining9. Summarizing itemsets10. Sequence mining11. Graph pattern mining12. Pattern and rule assessmentPart III. Clustering13. Representative-based clustering14. Hierarchical clustering15. Density-based clustering16. Spectral and graph clustering17. Clustering validationPart IV. Classification18. Probabilistic classification19. Decision tree classifier20. Linear discriminant analysis21. Support vector machines22. Classification assessmentPart V. Regression23. Linear regression24. Logistic regression25. Neural networks26. Deep learning27. Regression evaluation. Show more