Data mining : theories, algorithms, and examples
著者
書誌事項
Data mining : theories, algorithms, and examples
（Human factors and ergonomics）
CRC Press, c2014
- : hardcover
- : pbk
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注記
Includes bibliographical references (p. 319-322) and index
内容説明・目次
内容説明
New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms.
The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures.
The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them.
目次
AN OVERVIEW OF DATA MINING METHODOLOGIES: Introduction to data mining methodologies. METHODOLOGIES FOR MINING CLASSIFICATION AND PREDICTION PATTERNS: Regression models. Bayes classifiers. Decision trees. Multi-layer feedforward artificial neural networks. Support vector machines. Supervised clustering. METHODOLOGIES FOR MINING CLUSTERING AND ASSOCIATION PATTERNS: Hierarchical clustering. Partitional clustering. Self-organized map. Probability distribution estimation. Association rules. Bayesian networks. METHODOLOGIES FOR MINING DATA REDUCTION PATTERNS: Principal components analysis. Multi-dimensional scaling. Latent variable analysis. METHODOLOGIES FOR MINING OUTLIER AND ANOMALY PATTERNS: Univariate control charts. Multivariate control charts. METHODOLOGIES FOR MINING SEQUENTIAL AND TIME SERIES PATTERNS: Autocorrelation based time series analysis. Hidden Markov models for sequential pattern mining. Wavelet analysis. Hilbert transform. Nonlinear time series analysis.
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