Written for those with a science and engineering background, this book introduces and explains a comprehensive set of data mining techniques from various data mining fields. Concepts and methodologies are illustrated through numerous examples of data mining applications in cyber attack detection, discovery of neuronal population dynamics, and manufacturing quality control. Other topics include methodologies for mining classification and prediction patterns, mining clustering, and mining data reduction patterns and sequential and time series patterns.
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
Nong Ye is Professor of Industrial Engineering at Arizona State University in Tempe.