Data Mining: Theories, Algorithms, and Examples

Overview

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 ...

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Overview

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.

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Editorial Reviews

From the Publisher
"… 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

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Product Details

  • ISBN-13: 9781439808382
  • Publisher: Taylor & Francis
  • Publication date: 8/2/2013
  • Series: Human Factors and Ergonomics Series
  • Pages: 349
  • Product dimensions: 6.20 (w) x 9.20 (h) x 1.00 (d)

Meet the Author

Nong Ye is Professor of Industrial Engineering at Arizona State University in Tempe.

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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

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