Description: Data Mining by Nong Ye New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms.The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures. The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them. FORMAT Paperback LANGUAGE English CONDITION Brand New Author Biography Nong Ye is Professor of Industrial Engineering at Arizona State University in Tempe. Table of Contents AN OVERVIEW OF DATA MINING METHODOLOGIES: Introduction to data mining methodologies. METHODOLOGIES FOR MINING CLASSIFICATION AND PREDICTION PATTERNS: Regression models. Bayes classifiers. Decision trees. Multi-layer feedforward artificial neural networks. Support vector machines. Supervised clustering. METHODOLOGIES FOR MINING CLUSTERING AND ASSOCIATION PATTERNS: Hierarchical clustering. Partitional clustering. Self-organized map. Probability distribution estimation. Association rules. Bayesian networks. METHODOLOGIES FOR MINING DATA REDUCTION PATTERNS: Principal components analysis. Multi-dimensional scaling. Latent variable analysis. METHODOLOGIES FOR MINING OUTLIER AND ANOMALY PATTERNS: Univariate control charts. Multivariate control charts. METHODOLOGIES FOR MINING SEQUENTIAL AND TIME SERIES PATTERNS: Autocorrelation based time series analysis. Hidden Markov models for sequential pattern mining. Wavelet analysis. Hilbert transform. Nonlinear time series analysis. Review "… provides full spectrum coverage of the most important topics in data mining. By reading it, one can obtain a comprehensive view on data mining, including the basic concepts, the important problems in the area, and how to handle these problems. The whole book is presented in a way that a reader who do not have much background knowledge of data mining, can easily understand. You can find many figures and intuitive examples in the book. I really love these figures and examples, since they make the most complicated concepts and algorithms much easier to understand."—Zheng Zhao, SAS Institute Inc. , Cary, North Carolina, USA"… covers pretty much all the core data mining algorithms. It also covers several useful topics that are not covered by other data mining books such as univariate and multivariate control charts and wavelet analysis. Detailed examples are provided to illustrate the practical use of data mining algorithms. A list of software packages is also included for most algorithms covered in the book. These are extremely useful for data mining practitoners. I highly recommend this book for anyone interested in data mining."—Jieping Ye, Arizona State University, Tempe, USA "This is an excellent book for graduate students, professionals, or consultants who want to learn the different methods of data mining. The template that the author used: theory, example, software, references are very effective and efficient in conveying the general idea. The detailed examples are extremely helpful."–Stephen Hyatt, Northwestern Polytechnic University, Fremont, California, USA Review Quote "... provides full spectrum coverage of the most important topics in data mining. By reading it, one can obtain a comprehensive view on data mining, including the basic concepts, the important problems in the area, and how to handle these problems. The whole book is presented in a way that a reader who do not have much background knowledge of data mining, can easily understand. You can find many figures and intuitive examples in the book. I really love these figures and examples, since they make the most complicated concepts and algorithms much easier to understand." --Zheng Zhao, SAS Institute Inc. , Cary, North Carolina, USA "... covers pretty much all the core data mining algorithms. It also covers several useful topics that are not covered by other data mining books such as univariate and multivariate control charts and wavelet analysis. Detailed examples are provided to illustrate the practical use of data mining algorithms. A list of software packages is also included for most algorithms covered in the book. These are extremely useful for data mining practitoners. I highly recommend this book for anyone interested in data mining." --Jieping Ye, Arizona State University, Tempe, USA "This is an excellent book for graduate students, professionals, or consultants who want to learn the different methods of data mining. The template that the author used: theory, example, software, references are very effective and efficient in conveying the general idea. The detailed examples are extremely helpful." -Stephen Hyatt, Northwestern Polytechnic University, Fremont, California, USA Details ISBN1138073660 Year 2017 ISBN-10 1138073660 ISBN-13 9781138073661 Format Paperback Pages 349 Subtitle Theories, Algorithms, and Examples Place of Publication London Country of Publication United Kingdom Author Nong Ye Publisher Taylor & Francis Ltd DEWEY 006.312 Short Title Data Mining Language English Series Human Factors and Ergonomics Imprint CRC Press AU Release Date 2017-03-29 NZ Release Date 2017-03-29 Publication Date 2017-03-29 UK Release Date 2017-03-29 Illustrations 68 Tables, black and white; 57 Illustrations, black and white Alternative 9780367364861 Audience Tertiary & Higher Education We've got this At The Nile, if you're looking for it, we've got it. 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ISBN-13: 9781138073661
Book Title: Data Mining
Item Height: 234 mm
Item Width: 156 mm
Author: Nong Ye
Publication Name: Data Mining: Theories, Algorithms, and Examples
Format: Paperback
Language: English
Publisher: Taylor & Francis Ltd
Subject: Computer Science
Publication Year: 2017
Type: Textbook
Item Weight: 499 g
Number of Pages: 349 Pages