Mining time-changing data streams

@inproceedings{Hulten2001MiningTD,
  title={Mining time-changing data streams},
  author={Geoff Hulten and Laurie Spencer and Pedro M. Domingos},
  booktitle={KDD},
  year={2001}
}
Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over months or years, and the underlying processes generating them changed during this time, sometimes radically. Although a number of algorithms have been proposed for learning time-changing concepts, they generally do not scale well to very large databases… CONTINUE READING
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Density Estimation

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Mining time-chaning data streams

  • G. Hulten, L. Spencer, P. Domingos
  • Proc. 7th ACM SIGKDD Int. Conf. on Knowledge…
  • 2001
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