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Data mining : theories, algorithms, and examples

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