Normal view MARC view ISBD view

YE, Nong

Data mining : theories, algorithms, and examples - Boca Raton Taylor & Francis 2014 - xix, 329 P. 24 cm. - Human factors and ergonomics. .

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.

ISBN: 9781138073661

Subjects--Topical Terms:
Data mining.
Data mining -- Mathematical models.

Dewey Class. No.: 006.312 / YEN