Nong Ye

Data Mining

Theories, Algorithms, and Examples

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 349
• Erscheinungstermin: 29. März 2017
• Englisch
• Abmessung: 234mm x 156mm x 19mm
• Gewicht: 494g
• ISBN-13: 9781138073661
• ISBN-10: 1138073660
• Artikelnr.: 48809735

Autorenporträt

Nong Ye is Professor of Industrial Engineering at Arizona State University in Tempe.

Inhaltsangabe

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.