Data mining : theories, algorithms, and examples / Nong Ye

"A CRC title."

Includes bibliographical references and index

pt. 1. An overview of data mining. Introduction to data, data patterns, and data mining -- pt. 2. Algorithms for mining classification and prediction patterns. Linear and nonlinear regression models -- Naive Bayes classifier -- Decision and regression trees -- Artificial neural networks for classification and prediction -- Support vector machines -- k-Nearest neighbor classifier and supervised clustering -- pt. 3. Algorithms for mining cluster and association patterns. Hierarchial clustering -- K-Means clustering and density-based clustering -- Self-organizing map -- Probability distributions of univariate data -- Association rules -- Bayesian network -- pt. 4. Algorithms for mining data reduction patterns. Principal component analysis -- Multidimensional scaling -- pt. 5. Algorithms for mining outlier and anomaly patterns. Univariate control charts -- Multivariate control charts -- pt. 6. Algorithms for mining sequential and temporal patterns. Autocorrelation and time series analysis -- Markov chain models and hidden Markov models -- Wavelet analysis