Data Mining Theories, Algorithms, and Examples Human Factors and Ergonomics Series
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.
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.
Nong Ye is Professor of Industrial Engineering at Arizona State University in Tempe.
Date de parution : 08-2013
15.6x23.4 cm
Date de parution : 04-2017
15.6x23.4 cm
Thèmes de Data Mining :
Mots-clés :
Multivariate EWMA Control Chart; EWMA Statistic; Bayes classifiers; EWMA Control Chart; Multi-layer feedforward artificial neural networks; Data Set; Association rules; Training Data Records; Bayesian networks; Fourth Data Point; Univariate control charts; Single Fault Cases; Multivariate control charts; CUSUM Control Chart; Hidden Markov models; Frequent Item Sets; Wavelet analysis; Hilbert transform; Dummy Cluster; mining patterns; Control Charts; massive data; Training Data Set; data patterns; Multilayer Feedforward Ann; data mining algorithms; Bayesian Network; Shewhart Control Charts; Conditional Probability Distributions; Feedforward Ann; SVM Formulation; Markov Chain Models; Conditional Probability Table; Target Class; Decision Boundary; Binary Decision Tree