Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners

Index

A

• AdaBoost
• adaptive regression
• additive methods
• agglomerative clustering
• Akaike information criterion (AIC)
• Akka
• algorithms, future development of
• aligned box criteria (ABC)
• alternating least squares
• Amazon
• analytical tools
  • about
  • Java and Java Virtual Machine (JVM) languages
  • Python
  • R language
  • SAS system
  • Weka (Waikato Environment for Knowledge Analysis)
• analytics
• Apache Hadoop project
• Apple iPhone
• application program interface (API)
• Ashton, Kevin
• assessing
  • predictive models
  • quality of recommendation systems
• Auto-ID Center
• average linkage
• averaged squared error (ASE)
• averaging model process
• averaging process

B

• BackRub
• bagging
• banking case study
• Barksdale, Jim
• baseline model
• Bayes, Thomas
• Bayesian information criterion (BIC)
• Bayesian methods network classification
  • about
  • inference in Bayesian networks
  • learning Bayesian networks
  • Naive Bayes Network
  • parameter learning
  • scoring for supervised learning
• Bayesian network-augmented naive Bayes (BAN)
• Bayesian networks
  • inference in
  • learning
• Bayesians
• “Beowulf” cluster
• Bernoulli distribution
• big data
  • See also specific topics
  • benefits of using
  • complexities with
  • defined
  • fad nature of
  • future of
  • generalized linear model (GLM) applications and
  • popularity of term
  • regression analysis applications and
  • timeline for
  • value of using
• Big Data Appliance
• “Big Data Dynamic Factor Models for Macroeconomic Measurement and Forecasting” (Diebolt)
• Big Data Research and Development Initiative
• binary classification
• binary state
• binning ...