Data Mining

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A01=Nong Ye
advanced data mining methodologies
Association rules
Author_Nong Ye
Bayes classifiers
Bayesian inference
Bayesian Network
Bayesian networks
Binary Decision Tree
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Conditional Probability Distributions
Conditional Probability Table
Control Charts
CUSUM Control Chart
data mining algorithms
data patterns
Data Set
Decision Boundary
Dummy Cluster
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EWMA Control Chart
EWMA Statistic
Feedforward Ann
Fourth Data Point
Frequent Item Sets
Hidden Markov Models
Hilbert transform
machine learning models
Markov Chain Models
massive data
mining patterns
Multi-layer feedforward artificial neural networks
Multilayer Feedforward Ann
Multivariate control charts
Multivariate EWMA Control Chart
neural network algorithms
principal component analysis
regression analysis
sequential pattern mining
Shewhart Control Charts
Single Fault Cases
SVM Formulation
Target Class
Training Data Records
Training Data Set
Univariate Control Charts
Wavelet analysis

Product details

  • ISBN 9781439808382
  • Weight: 635g
  • Dimensions: 156 x 234mm
  • Publication Date: 26 Jul 2013
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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.

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

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