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Data Mining Methods and Applications

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advanced neural network forecasting
algorithm
Association Rule
Association Rules Mining
Bagging Ensemble
Benford's Law
Benford’s Law
Bpm
BSS
business intelligence
Category=UNF
Category=UTF
Category=UY
CIRP Freshman Survey
Data Mining
Data Mining Techniques
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eq_computing
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evolutionary algorithms
Exhaustive CHAID
Generalization Error
genetic
healthcare analytics
HTS
ICA
ICA Algorithm
ICA Model
Lift Chart
Linear Discrimination
logistic
market research methods
Misclassification Cost
multivariate analysis
network
neural
Neural Network
Neural Network Ensemble
Neural Network Model
predictive modeling
regression
Roc Curve
SAS Enterprise Miner
self-organized
set
techniques
training
Training Set Size

Product details

  • ISBN 9780849385223
  • Weight: 780g
  • Dimensions: 156 x 234mm
  • Publication Date: 22 Dec 2007
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

With today’s information explosion, many organizations are now able to access a wealth of valuable data. Unfortunately, most of these organizations find they are ill-equipped to organize this information, let alone put it to work for them.

Gain a Competitive Advantage

  • Employ data mining in research and forecasting
  • Build models with data management tools and methodology optimization
  • Gain sophisticated breakdowns and complex analysis through multivariate, evolutionary, and neural net methods
  • Learn how to classify data and maintain quality

Transform Data into Business Acumen

Data Mining Methods and Applications supplies organizations with the data management tools that will allow them to harness the critical facts and figures needed to improve their bottom line. Drawing from finance, marketing, economics, science, and healthcare, this forward thinking volume:

  • Demonstrates how the transformation of data into business intelligence is an essential aspect of strategic decision-making
  • Emphasizes the use of data mining concepts in real-world scenarios with large database components
  • Focuses on data mining and forecasting methods in conducting market research
About
Kenneth D. Lawrence, Stephan Kudyba, Ronald K. Klimberg