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Reading about data stream clustering I met the next terms:

  • landmark window model,
  • sliding window model,
  • damped window.

As to sliding window it's clear - oldest data escape the scope, the new data go inside. But what are the consepts of the other two?

I can suppose that damped window it's like a buffer, that is flushed after filling but some explanation, that I had found, states that dumped uses decreasing weights of data as function of time.

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  • $\begingroup$ Isn't "landmark data model" be supposed to be based on a "landmark" i.e. any given point, not the specifically the "beginning of times"? $\endgroup$ – LI AR Apr 21 '17 at 15:00
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From p 1077 of "Advanced Data Mining and Applications: Second International Conference":

A landmark data model considers the data in the data stream from the beginning until now.

A sliding window model, on the other hand, considers the data from now up to a certain range in the past.

A damped window model associates weights with the data in the stream, and gives higher weights to recent data than those in the past.

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