|aData mining :|btheories, algorithms, and examples /|cNong Ye.
260
|aBoca Raton :|bTaylor & Francis,|cc2014.
264
1
|aBoca Raton :|bTaylor & Francis,|c[2014]
300
|axix, 329 pages ;|c24 cm.
336
|atext|btxt|2rdacontent
337
|aunmediated|bn|2rdamedia
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|avolume|bnc|2rdacarrier
490
0
|aHuman Factors and Ergonomics Series
504
|aIncludes bibliographical references and index.
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|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.
內容簡介top Data Mining 簡介 Written for those with a science and engineering background, this book introduces and explains a comprehensive set of data mining techniques from various data mining fields. Concepts and methodologies are illustrated through numerous examples of data mining applications in cyber attack detection, discovery of neuronal population dynamics, and manufacturing quality control. Other topics include methodologies for mining classification and prediction patterns, mining clustering, and mining data reduction patterns and sequential and time series patterns.