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Mining frequent itemsets over tuple-evolving data streams
2013
Proceedings of the 28th Annual ACM Symposium on Applied Computing - SAC '13
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
doi:10.1145/2480362.2480419
dblp:conf/sac/ZhangHMZMM13
fatcat:vrnebwooirahhejqn56xwscwmy