Mining frequent itemsets over tuple-evolving data streams

Abstract : In many data streaming applications today, tuples inside the streams may get revised over time. This type of data stream brings new issues and challenges to the data mining tasks. We present a theoretical analysis for mining frequent itemsets from sliding windows over such data. We define conditions that determine whether an infrequent itemset will become frequent when some existing tuples inside the streams have been updated. We design simple but effective structures for managing both the evolving tuples and the candidate frequent itemsets. Moreover, we provide a novel verification method that efficiently computes the counts of candidate itemsets. Experiments on real-world datasets show the efficiency and effectiveness of our proposed method.
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Conference papers
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00830923
Contributor : Florent Masseglia <>
Submitted on : Thursday, June 6, 2013 - 9:50:15 AM
Last modification on : Tuesday, August 13, 2019 - 11:10:03 AM

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Chongsheng Zhang, Yuan Hao, Mirjana Mazuran, Carlo Zaniolo, Hamid Mousavi, et al.. Mining frequent itemsets over tuple-evolving data streams. SAC: Symposium on Applied Computing, Mar 2013, Coimbra, Portugal. pp.267-274, ⟨10.1145/2480362.2480419⟩. ⟨lirmm-00830923⟩

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