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Product Identifiers
PublisherCambridge University Press
ISBN-101108473989
ISBN-139781108473989
eBay Product ID (ePID)12038829556
Product Key Features
Number of Pages776 Pages
Publication NameData Mining and Machine Learning : Fundamental concepts and Algorithms
LanguageEnglish
SubjectDatabases / Data Mining, Databases / General
Publication Year2020
TypeTextbook
Subject AreaComputers
AuthorWagner Meira Jr., Mohammed J. Zaki
FormatHardcover
Dimensions
Item Height1.8 in
Item Weight55 Oz
Item Length10.1 in
Item Width7.3 in
Additional Product Features
Edition Number2
Intended AudienceScholarly & Professional
LCCN2019-037293
Reviews"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, ACM SIGKDD, the leading professional organization for Knowledge Discovery and Data Mining
Dewey Edition23
IllustratedYes
Dewey Decimal006.312
Table Of Content1. 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.
SynopsisThis textbook for senior undergraduate and graduate students offers comprehensive coverage, an algorithmic perspective, and a wealth of examples in exploratory data analysis, pattern mining, clustering, and classification. New to this second edition are several chapters on regression, including neural networks and deep learning., 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.
LC Classification NumberQA76.9.D343Z36 2020
