01834nam a2200241 a 4500001001400000003000700014005001700021008004100038020004300079020004000122040000800162100001300170245006500183260004400248300002600292490004000318500001900358504005000377505107200427650001601499650003701515830004001552vtls000085571KUKTEM20251125100128.0150409t2014    flu    f b    001 0 eng d  a9781439808382 (hardcover : alk. paper)  a1439808384 (hardcover : alk. paper)  aUMP1 aYe, Nong10aData mining :btheories, algorithms, and examples /cNong Ye  aBoca Raton :bTaylor & Francis,c[2014]  axix, 329 p. ;c24 cm.1 aHuman Factors and Ergonomics Series  a"A CRC title."  aIncludes bibliographical references and index0 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 -- 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 0aData mining 0aData miningxMathematical models 0aHuman Factors and Ergonomics Series