Data Mining

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

"… 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

• 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.