Mining frequent itemsets over tuple-evolving data streams

Authors:
Chongsheng Zhang;Mirjana Mazuran;Hamid Mousavi;Yuan Hao;Carlo Zaniolo;Florent Masseglia
Affiliations:
Henan University, Kaifeng, China;Politecnico di Milano, Milano, Italy;UCLA, Los Angeles;University of Science and Technology Beijing, China;UCLA, Los Angeles;Zenith team, INRIA, Montpellier, France
Venue:
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Year:
2013

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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.