Data Mining and Machine Learning: Fundamental Concepts and Algorithms

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Cambridge University Press, 2020 M01 30 - 776 páginas
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.
 

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Contenido

Numeric Attributes
29
Categorical Attributes
61
Graph Data
92
Kernel Methods
134
Highdimensional Data
163
Dimensionality Reduction
184
FREQUENT PATTERN MINING
217
Summarizing Itemsets
244
Spectral and Graph Clustering
394
Clustering Validation
426
CLASSIFICATION
467
Decision Tree Classifier
483
Linear Discriminant Analysis
501
Support Vector Machines
517
Classification Assessment
546
REGRESSION
587

Sequence Mining
261
Graph Pattern Mining
282
Pattern and Rule Assessment
303
CLUSTERING
332
Hierarchical Clustering
364
Densitybased Clustering
375
Logistic Regression
623
Neural Networks
637
Deep Learning
672
Regression Evaluation
720
Index
755
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Mohammed J. Zaki is Professor of Computer Science at Rensselaer Polytechnic Institute, New York, where he also serves as Associate Department Head and Graduate Program Director. He has more than 250 publications and is an Associate Editor for the journal Data Mining and Knowledge Discovery. He is on the Board of Directors for Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining (ACM SIGKDD). He has received the National Science Foundation CAREER Award, and the Department of Energy Early Career Principal Investigator Award. He is an ACM Distinguished Member, and IEEE Fellow.

Wagner Meira, Jr is Professor of Computer Science at Universidade Federal de Minas Gerais, Brazil, where he is currently the chair of the department. He has published more than 230 papers on data mining and parallel and distributed systems. He was leader of the Knowledge Discovery research track of InWeb and is currently Vice-chair of INCT-Cyber. He is on the editorial board of the journal Data Mining and Knowledge Discovery and was the program chair of SDM'16 and ACM WebSci'19. He has been a CNPq researcher since 2002. He has received an IBM Faculty Award and several Google Faculty Research Awards.

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