000			01677nam a22002057a 4500
999			_c51413 _d52960
003			ISURa
008			190528b xxu||||| |||| 00| 0 eng d
020			_a9781138073661
041			_aEnglish
082			_a006.312 _bYEN
100			_aYE, Nong _972291
245			_aData mining : theories, algorithms, and examples
260			_aBoca Raton _bTaylor & Francis _c2014
300			_axix, 329 P. _c24 cm.
440			_aHuman factors and ergonomics. _972292
520			_apt. 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 -- Naïve 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.
650			_aData mining. _929634
650			_aData mining -- Mathematical models. _972293
942			_2ddc _cLN