Research Area:  Big Data
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.
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Author(s) Name:  Carlos Fernandez-Basso,Abel J. Francisco-Agra,Maria J. Martin-Bautista and M. Dolores Ruiz
Journal name:  Knowledge-Based Systems
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Publisher name:  ELSEVIER
DOI:  10.1016/j.knosys.2018.09.026
Volume Information:  Volume 163, 1 January 2019, Pages 666-674
Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0950705118304775