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Product Identifiers
PublisherCambridge University Press
ISBN-100521766338
ISBN-139780521766333
eBay Product ID (ePID)168282499
Product Key Features
Number of Pages562 Pages
Publication NameData Mining and Analysis : Fundamental concepts and Algorithms
LanguageEnglish
SubjectDatabases / Data Mining, Databases / General
Publication Year2014
TypeTextbook
AuthorWagner Meira Jr., Mohammed J. Zaki
Subject AreaComputers
FormatHardcover
Dimensions
Item Height1.2 in
Item Weight42.3 Oz
Item Length10.2 in
Item Width7.2 in
Additional Product Features
Intended AudienceCollege Audience
LCCN2013-037544
Reviews"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). 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." Professor Christos Faloutsos, Carnegie Mellon University and winner of the ACM SIGKDD Innovation Award, "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.
SynopsisThe fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike. Key features: - Covers both core methods and cutting-edge research - Algorithmic approach with open-source implementations - Minimal prerequisites: all key mathematical concepts are presented, as is the intuition behind the formulas - Short, self-contained chapters with class-tested examples and exercises allow for flexibility in designing a course and for easy reference - Supplementary website with lecture slides, videos, project ideas, and more, The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike., The fundamental algorithms in data mining and analysis are the basis for business intelligence and analytics, automated methods to analyze patterns, and models of data. This textbook for senior undergraduate and graduate data mining courses provides a comprehensive overview from an algorithmic perspective, integrating concepts from machine learning and statistics.
LC Classification NumberQA76.9.D343 Z36 2014
