Data mining : theories, algorithms, and examples

By:

YE, Nong

Material type: Text

TextLanguage: English Language Series: Human factors and ergonomicsPublication details: Boca Raton Taylor & Francis 2014Description: xix, 329 P. 24 cmISBN:

9781138073661

Subject(s):

DDC classification:

006.312 YEN

Summary: 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 -- 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.

Item type:

Lending Books

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Lending Books	Applied Sciences Library Lending Section	Lending Collection	006.312 YEN (Browse shelf(Opens below))	Available		112972
Sheduled Reference	Applied Sciences Library Reference Section	Reference Collection	006.312 YEN (Browse shelf(Opens below))	Available		112973

Total holds: 0

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 --
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.

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