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Statistical Analysis of Massive Data Streams Proceedings of a Workshop (2004) / Chapter Skim
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Pages 342-343

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From page 342...
... Using this framework, we have successfully adapted several learning algorithms to massive data streams, including decision tree induction, Bayesian network learning, k-means clustering, and the EM algorithm for mixtures of Gaussians. These algorithms are able to process on the order of billions of examples per day using off-the-shelf hardware.
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From page 343...
... Within this framework, we have designed and implemented massive-stream versions of decision tree induction (Domingos & Hulten, 2000; Hulten et al., 2001) , Bayesian network learning (Hulten & Domingos, 2002)
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Key Terms

  • infinite data
  • decision tree
  • tree induction
  • decision trees
  • data streams

  • learning algorithms
  • data mining
  • finite data
  • data stream
  • massive data

  • mining data
  • network learning
  • bayesian network
  • mining massive
  • data rates

  • including decision


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