Mining Frequent Patterns in Data Streams at Multiple Time Granularities

Mining Frequent Patterns in Data Streams at Multiple Time Granularities (2002)

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by Chris Giannella , Jiawei Han , Jian Pei , Xifeng Yan , Philip S. Yu

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@MISC{Giannella02miningfrequent,
    author = {Chris Giannella and Jiawei Han and Jian Pei and Xifeng Yan and Philip S. Yu},
    title = {Mining Frequent Patterns in Data Streams at Multiple Time Granularities},
    year = {2002}
}

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Abstract

Although frequent-pattern mining has been widely studied and used, it is challenging to extend it to data streams. Compared to mining from a static transaction data set, the streaming case has far more information to track and far greater complexity to manage. Infrequent items can become frequent later on and hence cannot be ignored. The storage structure needs to be dynamically adjusted to reflect the evolution of itemset frequencies over time.

Citations

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