Synopsis
Data streams are an unbounded sequence of data records, whose entry rate is usually high and whose dispersions frequently change. Algorithms used for analyzing data streams must be able to process data in a very limited time frame and memory. This
is due to the fact that, unlike traditional data, entire data streams cannot be stored in the memory .
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Improvising Learning Imbalanced Data in Data Streams
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