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• W2141014803 abstract "Stream data arrives dynamically when stream continues, which cannot be reflected by the traditional transaction-based sliding window, so the processing efficiency is low. We build a timestamp-based sliding window model and propose a frequent itemset mining algorithm named FIMS. In this algorithm, we use an enumeration tree to store the data synopsis, as a result, the computational pruning can be conducted. The experimental results over a dataset present that our algorithm is effective and efficient." @default.
• W2141014803 created "2016-06-24" @default.
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• W2141014803 date "2011-07-01" @default.
• W2141014803 modified "2023-09-22" @default.
• W2141014803 title "Frequent itemset mining over time-sensitive streams" @default.
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• W2141014803 doi "https://doi.org/10.1109/fskd.2011.6019881" @default.
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