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- W2894634079 abstract "The amount of information generated in social media channels or economical/business transactions exceeds the usual bounds of static databases and is in continuous growing. In this work, we propose a frequent itemset mining method using sliding windows capable of extracting tendencies from continuous data flows. For that aim, we develop this method using Big Data technologies, in particular, using the Spark Streaming framework enabling distributing the computation along several clusters and thus improving the algorithm speed. The experimentation carried out shows the capability of our proposal and its scalability when massive amounts of data coming from streams are taken into account." @default.
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- W2894634079 date "2019-01-01" @default.
- W2894634079 modified "2023-10-18" @default.
- W2894634079 title "Finding tendencies in streaming data using Big Data frequent itemset mining" @default.
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- W2894634079 doi "https://doi.org/10.1016/j.knosys.2018.09.026" @default.
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