[内容简介]

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.

[目录]

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