000			02538nam a22002177a 4500
999			_c4122 _d4122
005			20221201122403.0
008			221201b ||||| |||| 00| 0 eng d
020			_a9781108473989
082			_a006.312 _bZAK
100			_aZaki, Mohammed J. _99323
245			_aData mining and machine learning: _bfundamental concepts and algorithms
250			_a2nd
260			_bCambridge University Press _aCambridge _c2020
300			_axii, 766 p.
365			_aGBP _b57.99
504			_aTable 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.
520			_aThe 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
650			_aData mining _9365
700			_aMeira, Wagner _910413
942			_2ddc _cBK