Info-fuzzy algorithms for mining dynamic data streams

  title={Info-fuzzy algorithms for mining dynamic data streams},
  author={Lior Cohen and Gil Avrahami-Bakish and Mark Last and Abraham Kandel},
  journal={Appl. Soft Comput.},
Most data mining algorithms assume static behavior of the incoming data. In the real world, the situation is different and most continuously collected data streams are generated by dynamic processes, which may change over time, in some cases even drastically. The change in the underlying concept, also known as concept drift, causes the data mining model generated from past examples to become less accurate and relevant for classifying the current data. Most online learning algorithms deal with… CONTINUE READING
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